Research on the Dynamic Evolution of the Cooperative Network of National Defense Decryption Patents

Research on the Dynamic Evolution of the Cooperative Network of National Defense Decryption Patents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on the Dynamic Evolution of the Cooperative Network of National Defense Decryption Patents Hongyu Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8510791/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the context of civil-military integration, the declassification of defense patents has become a crucial mechanism for enhancing technology transfer and maximizing the economic and social value of military innovations. However, the cooperative network dynamics and knowledge evolution of declassified defense patents remain underexplored. This study aims to investigate the structural evolution of defense decryption patent cooperation networks, identify key innovation entities, and analyze the knowledge flow within the defense patent system. Using data from the Wisdom Bud database, this research employs social network analysis to examine the cooperative relationships among defense patent innovation entities from 2000 to 2017. Key network indicators such as degree centrality, weighted degree, and network density are analyzed to understand the dynamic evolution of defense decryption patent cooperation. Furthermore, the study constructs a knowledge network to explore the interconnections and combinational potential of different knowledge elements in defense decryption patents. The results reveal that the cooperative network of defense decryption patents has undergone a dynamic evolution, transitioning from isolated collaborations to a more interconnected and diversified cooperation model. Star nodes, such as leading defense research institutes and universities, play a central role in maintaining stable cooperation relationships and driving innovation. Additionally, the knowledge network analysis shows that knowledge elements in defense decryption patents are increasingly interlinked, demonstrating high potential for technological recombination and innovation. This research contributes to the understanding of defense technology transfer by integrating the analysis of individual networks, cooperative networks, and knowledge network evolution. The findings provide valuable insights for policymakers and enterprises to optimize defense patent collaboration, enhance knowledge flow, and promote civil-military integration. Future research could further explore international defense decryption patents and the strategic value of their patent texts in a global context. Humanities/Complex networks Social science/Complex networks Physical sciences/Mathematics and computing Social science/Science technology and society Defense decryption patents Cooperative network Knowledge network Social network analysis Civil-military integration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Patents serve as critical instruments for safeguarding technological innovations, playing a vital role in economic transformation and sustainable development. Unlike ordinary patents, defense patents are closely linked to national security and are often classified to prevent unauthorized access. However, declassifying and commercializing these patents can significantly enhance their economic and societal value. In recent years, several countries have leveraged patent transfer mechanisms to promote the transformation of defense technologies into civilian applications (Acosta et al., 2020 ). For example, the United States implemented the Bayh-Dole Act, which allows private entities to retain ownership of patents developed through federally funded research, thus fostering innovation and technology transfer (Mowery et al., 2001 ). Similarly, South Korea has adopted civil-military integration policies to enhance national defense capabilities while stimulating economic growth (Drifte, 1997 ) In China, the Military-Civil Fusion (MCF) strategy has been elevated to a national priority, aiming to integrate military and civilian sectors for innovation and technological advancement (Kania, 2019 ). The Chinese government has actively promoted the declassification of defense patents to facilitate their commercialization. As of June 2023, a total of 7,656 defense patents have been declassified, reflecting the country's commitment to enhancing the transfer of military technologies to the civilian sector (Agranat & Marom, 2020 ). However, despite these initiatives, several challenges remain. The lack of systematic research on the structural evolution of defense decryption patent collaboration networks, the changing roles of key innovation entities, and the interconnections within defense patent knowledge networks limit our understanding of how these patents contribute to technological progress and industrial development. Existing studies suggest that military-civilian patent collaboration networks play a crucial role in accelerating defense technology transfer (Sullivan, 1989 ). Through strategic partnerships, enterprises gain access to a broader pool of resources, fostering industry-university-research collaborations that optimize the innovation ecosystem (Hu et al., 2023 ; Wu & Xu, 2022 ). However, there remains a gap in understanding how defense decryption patent cooperation networks evolve over time, which entities play central roles in these networks, and how knowledge elements interact within the system. Addressing these gaps is essential for maximizing the impact of declassified defense patents on national innovation capabilities. This study aims to analyze the basic characteristics of declassified defense patents, investigate the dynamic evolution of their cooperation networks using social network analysis, and explore the development of their knowledge networks. By integrating individual networks, cooperative networks, and knowledge network evolution, this research provides a comprehensive understanding of the collaboration and knowledge dynamics within defense decryption patents. The findings offer valuable insights for policymakers and industry stakeholders to optimize the allocation of innovation resources and enhance civil-military integration strategies. The remainder of this paper is structured as follows: Section 2 presents a literature review on defense patent collaboration and knowledge networks. Section 3 describes the data sources and methodologies employed in this study. Section 4 analyzes the dynamic evolution of defense decryption patent cooperation networks, while Section 5 explores the development of their knowledge networks. Section 6 discusses the implications of the findings and provides recommendations for future research and policy formulation. The declassification and subsequent integration of defense patents into civilian industries have been pivotal in advancing technological innovation and economic development across various nations. In March 2017, China declassified over 3,000 national defense patents, marking a significant move to promote civil-military integration (CMI) and enhance civilian participation in defense technology (International Institute for Strategic Studies, 2018). This initiative aimed to bridge the gap between military advancements and civilian applications, fostering a symbiotic relationship between the two sectors. 2 Literature Review The dynamic evolution of collaborative innovation networks is central to understanding technological progress. These networks, comprising enterprises, universities, research institutions, government entities, and social service systems, evolve through changes in relationships and resource exchanges among participants. Factors such as complementary resources, member changes, and preferential link mechanisms drive this evolution, leading to structural transformations within the network (Zeng et al., 2019). In the context of defense technology, the declassification of patents has introduced new actors into the innovation network, thereby altering its dynamics and fostering novel collaborations. Knowledge networks, which represent the intricate web of knowledge elements and their interconnections, play a crucial role in the innovation process. The system of scientific innovation can be characterized as a complex, multi-layered network of actors, their products, and knowledge elements. Despite the progress that has been made, a more comprehensive understanding of the interactions and dynamics of this multi-layered network remains a significant challenge (Frontiers in Physics, 2024). In the realm of defense patents, the integration of declassified knowledge into civilian industries necessitates a thorough understanding of these knowledge networks to effectively harness and build upon existing innovations. The interplay between collaborative networks and knowledge networks is evident in the concept of embeddedness, which examines how the position of entities within a network influences their collaborative behaviors and innovation outcomes. Research indicates that both the collaborative interactions of organizations and their knowledge element exchanges significantly impact the dynamic changes of collaborative innovation networks. By analyzing multiple collaboration and knowledge networks, studies have revealed how social and knowledge network embeddedness affects collaboration dynamics (Chen et al., 2023). This perspective is particularly relevant in the context of declassified defense patents, where the integration of new knowledge elements into existing networks can lead to the emergence of innovative collaborations and technological advancements. In summary, the declassification of defense patents serves as a catalyst for the dynamic evolution of collaborative innovation and knowledge networks. Understanding the mechanisms underlying these evolutions is essential for effectively leveraging declassified knowledge to drive technological innovation and economic growth. 3. Methodology 3.1 Data Source Patent records were retrieved from the Wisdom Bud (ZhiHuiYa) patent database. Access to Wisdom Bud was provided through a licensed institutional subscription held by Guangzhou College of Technology and Business. We queried declassified Chinese national defense patents (“defense decryption patents”) published between 1992 and 2017. The search strategy combined keywords, International Patent Classification (IPC) codes, and applicant/inventor fields. Bibliographic and technical metadata were exported, including patent title/abstract, application and publication numbers, applicants, inventors, filing/publication dates, and IPC codes. Data cleaning was conducted to ensure consistency and suitability for network construction: duplicate records were removed; patents with missing key metadata (e.g., applicants/inventors, IPC codes, or year information) were excluded; and records outside the 1992–2017 time window were removed. For the cooperation-network analyses, we further retained patents involving at least two collaborating entities, yielding 728 cooperative patents. Permissions and ethics. Wisdom Bud is a third-party database; the authors had permission to access and analyze the data for research purposes under the database license/terms of use. The study relies on declassified patent records and does not involve human participants, human data, or animals; therefore, ethics approval and informed consent were not required. 3.2 Research Variables and Metrics Calculation In social network analysis (SNA), the fundamental elements are nodes and edges. In this study, nodes represent patent applicants or inventors, while edges denote the collaborative relationships between them. To analyze the structure and characteristics of the collaboration network, we calculated several key metrics. Degree Centrality measures the number of direct connections a node has, indicating its activity level within the network. Weighted Degree Centrality accounts for the strength of connections by assigning weights to edges, providing a more nuanced view of a node's importance. Clustering Coefficient assesses the extent to which a node's neighbors are interconnected, reflecting the tendency of nodes to form tightly knit groups. Average Path Length calculates the mean number of steps along the shortest paths for all possible pairs of network nodes, offering insights into the efficiency of information or resource flow within the network. Network Density represents the ratio of actual connections to all possible connections in the network, highlighting the overall level of connectivity. Collectively, these metrics provide a comprehensive understanding of the network’s topology and the roles of individual entities within it. 3.3 Analysis Method We employed social network analysis techniques to examine the collaborative structures among entities involved in declassified defense patents. Gephi, an open-source network analysis and visualization tool, was utilized for this purpose. Gephi supports various network metrics calculations and provides dynamic visualization capabilities, making it suitable for analyzing complex networks (Gephi, n.d.). The analysis process involved importing the cleaned dataset into Gephi, where nodes and edges were defined based on the relationships among patent applicants and inventors. Subsequently, the aforementioned metrics were computed to evaluate the network's properties. Visualizations were generated to illustrate the network's structure, highlighting key nodes and clusters that play pivotal roles in the collaborative landscape of declassified defense patents. 3.4 China’s Defense Patent System and Key Legal Regulations China’s defense patent system is designed to protect intellectual property related to national security while promoting the commercialization of defense technologies. The foundation of this system was established with the Regulations on National Defense Patents (1990), which define defense patents as those involving national defense interests and requiring confidentiality (State Council, 1990 ). Under these regulations, the National Defense Intellectual Property Office oversees the review and approval process for defense patents, ensuring compliance with security protocols and technological development goals. To strengthen intellectual property management in defense research and development (R&D), the National Intellectual Property Strategy Outline (2008) introduced a coordinated mechanism to regulate defense patent ownership, licensing, and incentives (State Council, 2008 ). This policy aimed to integrate intellectual property management into national defense R&D, manufacturing, and procurement processes, enhancing the utilization of military technologies in civilian industries. More recently, China’s 14th Five-Year Plan for Intellectual Property Protection and Utilization (2021) further emphasized the modernization of defense-related IP regulations. It called for an improved legal framework to facilitate technology transfer and commercialization while maintaining national security safeguards (State Council, 2021 ). The policy highlights the need for effective cooperation between military and civilian sectors, ensuring that declassified defense patents can contribute to broader technological advancements. Through these progressive legal and policy measures, China has established a structured framework that balances national security interests with innovation-driven military-civil integration. 3.5 Temporal Characteristics 3.5.1 Cooperation Quantity and Node Temporal Characteristics Due to the confidentiality of the discipline, relevant research invention patents are mainly self-applied, and the total number of defense decryption cooperative patents is relatively small. Specifically, from 1992 to 2017, the total number of defense decryption cooperative patents was 728. Among them, the total number of defense decryption cooperative patents from 1992 to 1999 was 44, accounting for only 6.04% of the total; from 2000 to 2009, the total number of defense decryption cooperative patents was 66, accounting for only 9.07% of the total; and from 2010 to 2017, the total number of defense decryption cooperative patents was 618, accounting for 83.79% of the total. The above conclusions are corroborated by Fig. 1 and Table 1 . Before 2000, the number of defense decryption cooperative patents was relatively low, with the number of applications and authorized defense decryption cooperative patents not exceeding 25 per year, showing a relatively flat and minor fluctuating curve. The main reasons are that the second revision of the “Patent Law” was conducted in 2000, and before this, patent policies were not sufficiently perfected, and patent cooperation did not receive enough attention from innovation entities. The statistical results show that before 2000, the network structure of defense decryption cooperative patents was loose, with few innovative entities participating in cooperation, and cooperation between innovative entities was relatively singular. After 2000, the number of defense decryption cooperative patents increased rapidly due to the continuous development of technology and science, where single entities could not meet practical needs, and when the knowledge and technology foundation was insufficient to support existing R&D needs, entities turned to seek more cooperative partners, leading to increased organizational cooperation. Since recent years' patents have not been fully authorized, the number of patents has dramatically decreased. Therefore, to ensure the accuracy of the cooperative network evolution analysis, this paper only selects the years after 2000 for analysis, with the research sample being from 2000 to 2017. Given the overall low number of defense decryption cooperative patents, each research sample interval is analyzed every two years. Table 1 1992–2017 number of national defense decryption cooperation patents and number of participating cooperation nodes Year Number of Patents Number of Nodes 1993 2 2 1995 8 3 1997 24 12 1999 10 10 2001 8 8 2003 18 11 2005 10 6 2007 6 6 2009 24 19 2011 52 24 2013 64 34 2015 36 23 2017 466 118 3.5.2 Temporal Characteristics of Core Patent Applicants Figure 2 presents a heat map of patent applications by the top ten patent applicants over the analyzed period. The Beijing Institute of Technology maintained steady patent activity throughout all time periods, while the Xi’an Institute of Modern Chemistry exhibited a sharp surge between 2012 and 2017, reaching 208 patents, making it the most active applicant. Northwestern Polytechnical University and the China Air-to-Air Missile Research Institute showed significant growth after 2002, particularly between 2007 and 2012, while the China Academy of Launch Vehicle Technology saw gradual increases, peaking at 109 patents between 2012 and 2017. The Beijing Spacecraft General Design Department and Chinese National University of Defense Technology steadily increased their contributions after 2002, while the 54th Research Institute of China Electronics Technology Group Corporation grew significantly from 2007 onward. Notably, the Shanghai Institute of Aerospace Systems Engineering and the 23rd Research Institute of China Aerospace Science and Industry Corporation emerged as key contributors after 2007, reflecting an increasing focus on aerospace and defense technologies in recent years. 3.5.3 Temporal Characteristics of IPC Patent Classification Figure 3 illustrates the temporal characteristics of IPC patent classifications from 1992 to 2017. Initially, C class (chemistry, metallurgy) had the most patents, which was later overtaken by F class (mechanical engineering), and recently, G class (physics) has had the most patents. This trend likely reflects shifts in technological focus and research directions. F (mechanical engineering), G (physics), and H (electricity) classes show significant growth trends, representing key areas of current and future research. An analysis of IPC classification numbers in Fig. 4 reveals that the top ten most frequently appearing IPC classification codes mainly focus on measurement and testing (G01), computing technology (G06), and telecommunications technology (H04), indicating these fields are the current research or patent application priorities. 4 Evolution of Cooperative Network Structure 4.1 Overall Evolution of National Defense Decryption Patent Cooperation Network Based on the selected data of defense decryption cooperative patents from 2000 to 2017, the national defense decryption patent cooperation network is constructed with each two-year period as a sample interval. Each subject involved in cooperation is taken as source and target nodes, and their connections are taken as edges to construct the cooperation network. Figure 5 illustrates the dynamic evolution process of the national defense decryption cooperation network from 2000 to 2017, with larger nodes indicating more entities cooperating with the node and thicker edges indicating more frequent cooperation between the nodes. Overall, the national defense decryption patent cooperation network is continually growing and developing, with an increasing number of entities participating in cooperation and strengthening relationships among entities, making the network more complete. Specifically, in the network structure from 2001 to 2010, the number of innovative entities participating in cooperation increased year by year, and their relationships became more intricate, playing a good transitional role over these ten years. In the 2010s, the national defense decryption patent cooperation network became more complex, with an increasing number of innovative entities participating in cooperation and more complex cooperation relationships, with some entities cooperating with multiple others, becoming "bridges" in the cooperation network. This promotes indirect cooperation between other entities, pushing for "multi-entity-multi-entity" collaboration, thereby expanding the scope of defense decryption patent cooperative innovation. The most representative entities are the "China Academy of Launch Vehicle Technology" and "Beijing Institute of Technology," as shown in Fig. 5 . In line with the research theme of this paper, the social network indicators used are shown in Table 2 . Table 2 Indicator Selection and Explanation Name Meaning Number of Network Nodes The number of entities participating in the network. Number of Network Edges The number of connections between nodes, representing the breadth of cooperation. Number of Network Connections The number of connections between nodes, representing the depth of cooperation. For example, if A and B have cooperated four times, their number of edges is 1, and the number of connections is 4. Network Degree The number of other entities a node cooperates with. Weighted Network Degree The total number of cooperation instances a node has. Average Degree The average degree of the network, representing the breadth of cooperation. Average Weighted Degree The average weighted degree of the network, representing the depth of cooperation. Network Density The ratio of actual to potential edges in the network, indirectly representing the scale of cooperation. Network Diameter The maximum shortest path length between any two nodes in the network. Average Path Length The average shortest path length between any two nodes, indicating ease of connection. Using Gephi 0.10.1 software, the overall structural indicator values of the national defense decryption patent cooperation network were calculated, as shown in Table 3 . Table 3 Overall Indicators of National Defense Decryption Patent Cooperation Year Number of Nodes Number of Edges Number of Connections Average Degree Average Weighted Degree Network Density Network Diameter Average Path Length 1993 2 1 1 1.000 1.000 1.000 1 1.000 1995 3 2 4 1.333 2.667 0.667 2 1.333 1997 12 9 12 1.500 2.000 0.136 1 1.000 1999 10 5 5 1.000 1.000 0.111 1 1.000 2001 8 4 4 1.000 1.000 0.143 1 1.000 2003 11 9 9 1.636 1.636 0.164 2 1.182 2005 6 5 5 1.667 1.667 0.333 2 1.167 2007 6 3 3 1.000 1.000 0.200 1 1.000 2009 19 10 12 1.053 1.263 0.058 2 1.091 2011 24 19 26 1.583 2.000 0.069 2 1.208 2013 34 23 33 1.353 1.941 0.041 2 1.115 2015 23 12 18 1.043 1.391 0.047 2 1.077 2017 118 77 233 1.305 3.847 0.011 4 1.640 The number of nodes measures the network scale, indicating the number of entities participating in the network. From the network scale (total nodes), except for a few years, the number of nodes has increased annually, reaching 118 in 2017, indicating a yearly increase in the number of participating entities. The number of edges represents the number of connections between nodes, and the number of connections represents the number of connections between nodes, indicating the depth of cooperation. For instance, if A and B have cooperated four times, their number of edges is 1, and the number of connections is 4. Combining Fig. 5 and Table 3 , the number of edges has also increased annually, except for a few years, indicating an increase in the actual connections in the network. The connections increased from 4 in the first phase to 77, indicating more diversified cooperation modes, evolving from "single entity-single entity" cooperation to "multi-entity-multi-entity" cooperation (see Fig. 6). Comparing the number of edges and connections reveals that before 2010, the connections and edges were similar, with many years having equal connections and edges, indicating that before 2010, each entity generally only cooperated once with its partner, without regular cooperation. After 2010, the difference between connections and edges gradually increased, indicating that cooperation between entities and fixed partners increased, reaching a relatively stable cooperation state. Average degree represents the average number of connections each node has in the network. A higher average degree means more nodes are connected to each node in the network. Average weighted degree reflects the average connection strength based on the number of connections, with higher values indicating more frequent connections between nodes. Overall, the average degree remained between 1 and 1.667, indicating that each node had less than two connections on average. Except for a few years, the average weighted degree remained below 2, indicating a maximum average connection of two between nodes. In 2016–2017, the average weighted degree reached a maximum of 3.847, indicating a maximum average connection of nearly four during this period. This suggests that the depth of cooperation in the defense decryption patent cooperation network still needs to be strengthened, promoting more cooperation between different entities. Network density describes the extent of interconnection among nodes. Overall, network density showed a downward trend, as an increase in the number of nodes and the breadth of connections led to a decrease in density. Specifically, from 2000 to 2007, network density was greater than 0.1, fluctuating between 0.143 and 0.333. After 2008, network density fell below 0.1, maintaining a relatively stable level between 0.041 and 0.069 from 2008 to 2015, and reaching the lowest value of 0.011 in 2016–2017 due to increased network breadth and potential connections. Network density results indicate that although cooperation relationships in the defense decryption patent cooperation network have improved, the current connection strength and breadth are insufficient to achieve a fully connected network. Thus, defense decryption patent cooperation needs to expand cooperation breadth further while maintaining depth, promoting knowledge sharing among multiple entities, and accelerating the integration of heterogeneous capital. Network diameter refers to the maximum shortest path length between any two nodes, and average path length indicates the average shortest path length between any two nodes. As network density decreases, network diameter and average path length increase, indicating greater effort and longer paths to establish connections between nodes. This also means that information can be transmitted over greater distances, promoting knowledge sharing among different entities and accelerating the integration of heterogeneous resources. 4.2 Evolution of Individual Networks in National Defense Decryption Patent Cooperation In the national defense decryption patent cooperation network, star nodes refer to central nodes that receive significant attention, similar to "stars" in a particular field. These nodes exhibit high innovation performance, are more visible to other nodes due to their strategic positions, and are preferred partners in new cooperation opportunities. To identify and analyze these star nodes, this study employs several centrality indicators that measure different aspects of a node's influence within the network. Degree centrality measures the number of direct connections a node has, indicating its level of activity within the network. A higher degree centrality suggests that the node collaborates with more entities, making it a key player in the cooperation network. It is calculated as: $$\:{C}_{D}\left(i\right)={k}_{i}$$ where \(\:{C}_{D}\left(i\right)\) represents the degree centrality of node \(\:i\) , and \(\:{k}_{i}\) is the number of direct connections that node \(\:i\) has. Betweenness centrality quantifies the extent to which a node acts as a bridge between other nodes, controlling the flow of information and collaboration opportunities. A high betweenness centrality indicates that the node plays a crucial intermediary role in connecting different parts of the network. It is defined as: $$\:{C}_{B}\left(i\right)={\sum\:}_{s\ne\:i\ne\:t}\frac{{\sigma\:}_{st}\left(i\right)}{{\sigma\:}_{st}}$$ where \(\:{\sigma\:}_{st}\) is the number of shortest paths between nodes \(\:s\) and \(\:t\) , and \(\:{\sigma\:}_{st}\left(i\right)\) is the number of those paths passing through node ii. Closeness centrality measures how efficiently a node can reach all other nodes in the network. A node with high closeness centrality is well-positioned to access and disseminate information quickly. It is calculated as: $$\:{C}_{C}\left(i\right)=\frac{1}{{\sum\:}_{j}d\left(i,j\right)}$$ where \(\:d\left(i,j\right)\) is the shortest path distance between node \(\:i\) and node \(\:j\) . Eigenvector centrality assesses a node’s importance based on its connections to other well-connected nodes. Unlike degree centrality, which considers only direct connections, eigenvector centrality accounts for the overall influence of a node within the entire network. It is computed by solving the eigenvector equation: $$\:\mathbf{A}\mathbf{x}=\lambda\:\mathbf{x}$$ where \(\:\mathbf{A}\) is the adjacency matrix of the network, \(\:\lambda\:\) is the largest eigenvalue, and \(\:\mathbf{x}\) is the eigenvector whose components indicate the centrality scores of the corresponding nodes. In this study, we calculate these centrality measures for each node and define the top five nodes with the highest scores in each period as star nodes . By analyzing their characteristics and temporal evolution, we gain valuable insights into the role of key innovation entities in the national defense decryption patent cooperation network and their impact on the network’s development. Table 4 National Defense Decryption Patent Cooperation 2001 2003 2005 Node Degree Node Degree Node Degree PLA Chongqing Garrison District 1(1) Beijing Institute of Technology 3(3) PLA Guangzhou Military Region Engineering Research and Design Institute 2(2) Beijing North Dahua Technology Co., Ltd. 1(1) PLA General Staff Department Fifth Institute 2(2) Chinese Academy of Sciences National Astronomical Observatory 2(2) PLA Armored Engineering Institute 1(1) China Electronics Equipment System Engineering Corporation 2(2) Hangzhou Institute of Electronic Industry 2(2) CEIC Asset Management Limited 1(1) Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences 2(2) Dalian Naval Academy of the Chinese People's Liberation Army Navy 2(2) Guangzhou Military Region Engineering Research and Design Institute 1(1) Anhui Hongxing Machinery Factory 2(2) China North Industries Group Corporation Qinhuangdao Yao Glass Fiber Reinforced Plastic Factory 1(1) 2007 2009 2011 Node Degree Node Degree Node Degree National Time Service Center, Chinese Academy of Sciences 1(1) Northwestern Polytechnical University 2(2) Northwestern Polytechnical University 4(7) Chinese Academy of Sciences National Astronomical Observatory 1(1) PLA Unit 92537 1(2) Shenyang Aircraft Corporation 3(6) Northwestern Polytechnical University 1(1) Rizhao Hatton Microarc Oxidation Co., Ltd. 1(2) Harbin Institute of Technology 2(4) Baoji Rare Metals Equipment Design Research Institute 1(1) Panda Electronics Group Co., Ltd. 1(2) Baosteel Special Steel Division 2(4) Beijing Institute of Technology 1(1) Nanjing Panda Electronics Co., Ltd. 1(2) Beijing University of Aeronautics and Astronautics 2(2) 2013 2015 2017 Node Degree Node Degree Node Degree Northwestern Polytechnical University 4(4) China Electronics Equipment System Engineering Corporation 2(2) China Academy of Launch Vehicle Technology 8(124) Baosteel Special Steel Division 3(3) Beijing Precision Mechanical and Electrical Control Equipment Research Institute 1(3) Beijing Institute of Technology 6(6) Harbin Institute of Technology 3(3) China Academy of Launch Vehicle Technology 1(3) China North Industries Group Corporation 203 Research Institute 3(12) Shenyang Aircraft Corporation 3(3) China Shipbuilding Industry Corporation No. 704 Research Institute 1(2) Xi'an Guidance Technology Co., Ltd. 3(12) Panda Electronics Group Co., Ltd. 2(4) PLA Naval Equipment Research Institute Ship Demonstration Research Institute 1(2) Shanghai Jiao Tong University 3(9) (Note: Numbers in parentheses indicate weighted degree. When two enterprises have the same degree ranking, the weighted degree is used for further ranking.) Due to the limited cooperation patent data before 2000, this paper only calculates data after 2000. As shown in Table 4 , except for a few periods, the degree and weighted degree of star nodes in each period are relatively high, being the network leaders. Specifically, in 2000–2001 and 2006–2007, the degree of star nodes was 1, indicating inactive networks where star nodes had only one connection with another entity. In other periods, the degree of top-ranking star nodes was greater than 1, indicating cooperation with multiple partners. Overall, star nodes' positions have become increasingly prominent over time. Particularly in 2016–2017, the degree of China Academy of Launch Vehicle Technology reached 8, and its weighted degree reached 124, while Beijing Institute of Technology's degree reached 6, ranking first and second among star nodes. This indicates their cooperation with various entities and stable cooperation with specific entities, such as China Academy of Launch Vehicle Technology's 58 collaborations with Beijing Aerospace Automatic Control Institute and 30 collaborations with Beijing Aerospace Long March Flying Vehicle Research Institute, representing typical multi-entity cooperation. 5 Evolution of Knowledge Network in National Defense Decryption Patents In China, patent applications typically adopt the International Patent Classification (IPC) system to categorize technologies and identify technical domains. This study extracted the first four digits of IPC classification numbers to identify 648 distinct knowledge elements. Based on the relationships between these elements, we constructed a knowledge network for national defense decryption patents, employing Social Network Analysis (SNA) to analyze its structural properties. 5.1 Data Integration and Network Construction Each knowledge element can exist independently, indicating that it contributes to patents without forming direct technological collaborations. However, knowledge elements may also form knowledge combinations, represented by edges in the network, which signify technological convergence when two or more elements appear within the same patent. Furthermore, connections between knowledge elements and defense innovation entities indicate ownership relationships, showing which institutions possess specific technological capabilities. This approach aligns with patent network methodologies that utilize IPC-based mapping to study technological knowledge flow and innovation networks (Lee & Kim, 2020 ). The co-occurrence of IPC codes within patents reflects the blending of distinct technologies, leading to interdisciplinary innovation and technology diffusion (Liu & Lu, 2012 ). 5.2 Analytical Methods and Calculation Formulas To quantify the structural properties of the knowledge network, we applied several key network analysis metrics commonly used in technological knowledge network studies (Abbasi et al., 2012 ). These metrics help to identify the central knowledge elements, the degree of knowledge integration, and the role of defense entities in technological convergence. 5.2.1 Degree Centrality Degree centrality measures how well a knowledge element is connected within the network. A higher degree centrality indicates that the knowledge element frequently co-occurs with other elements, making it more influential in shaping technological advancements (Freeman, 1977 ). The formula for degree centrality is: $$\:{C}_{D}\left(i\right)={k}_{i}$$ where \(\:{C}_{D}\left(i\right)\) represents the degree centrality of node \(\:i\) , and \(\:{k}_{i}\) ​ is the number of edges connected to node \(\:i\) . 5.2.2 Betweenness Centrality Betweenness centrality assesses how often a knowledge element acts as a bridge in the shortest paths between other elements. A high betweenness centrality suggests that the element plays a key role in linking different technological fields , facilitating knowledge transfer and innovation recombination (Brandes, 2001 ). The formula is: $$\:{C}_{B}\left(i\right)={\sum\:}_{s\ne\:i\ne\:t}\frac{{\sigma\:}_{st}\left(i\right)}{{\sigma\:}_{st}}$$ ​ where \(\:{\sigma\:}_{st}\) ​ is the total number of shortest paths from node \(\:s\) to node \(\:t\) , and \(\:{\sigma\:}_{st}\left(i\right)\) represents the number of those paths that pass through node \(\:i\) . 5.2.3 Network Density Network density measures the degree of interconnectivity within the knowledge network. A higher density value indicates a more interconnected technological landscape, which accelerates knowledge exchange and innovation diffusion (Abbasi et al., 2012 ). The formula is: $$\:D=\frac{2E}{N\left(N-1\right)}$$ where \(\:D\) represents network density, \(\:E\) is the number of edges in the network, and \(\:N\) is the total number of nodes. 5.3 Theoretical Basis and Literature Support The knowledge recombination theory suggests that technological advancement occurs through the integration of diverse knowledge elements. IPC-based networks provide a valuable tool to map technological convergence and predict emerging innovations (Liu & Lu, 2012 ). Previous research has demonstrated that central knowledge elements in patent networks drive interdisciplinary collaboration and contribute to technological breakthroughs (Freeman, 1977 ; Lee & Kim, 2020 ). The use of social network analysis in patent studies has been widely adopted to analyze innovation ecosystems, revealing how defense-related technologies evolve and integrate with civilian applications (Abbasi et al., 2012 ). By employing this methodology, our study provides a quantitative foundation for understanding how national defense decryption patents foster technological innovation and interdisciplinary knowledge exchange. 5.4 Overall Evolution of the Knowledge Network in National Defense Decryption Patents Using Gephi 0.10.1 software, the overall knowledge network of national defense decryption patents from 2000 to 2017 was visualized, as shown in Fig. 7 . From the figure, it is evident that there is communication and exchange between different technologies each year. Overall, the knowledge network of national defense decryption patents in China is continuously improving, with an increasing number of technological types participating in cooperation and strengthening relationships among technologies, making the network more complete. Entering the new century, with the second revision of the "Patent Law," China's national defense decryption patent knowledge cooperation network continuously improved, with annual increases in cooperation among technological fields, especially after 2005. More technological fields participated in cooperation, making the knowledge network increasingly complex. China's national defense decryption patent knowledge cooperation network gradually transitioned from a "single-knowledge-single-knowledge" model to a "multi-knowledge-multi-knowledge" model, with representative knowledge fields being B64G and G01M, as shown in Fig. 7 . Gephi 0.10.1 software calculated overall indicators for each sample interval, showing that the utilization of knowledge in national defense decryption patents has steadily increased. Particularly after entering the 21st century, the knowledge network's scale expanded rapidly, peaking at 206 nodes in 2013, accounting for 31.79% of all knowledge elements. This indicates a rapid increase in the number of knowledge elements participating in patent applications as innovation entities delve deeper into new knowledge. However, less than half of the knowledge elements are still utilized in innovation activities, indicating significant potential for future growth. From 2016 to 2017, the knowledge network's scale decreased significantly, possibly due to some patent applications not yet being authorized, affecting the data. Overall, after 2011, the knowledge network's scale remained between 150 and 210, showing that defense innovation entities gradually found a suitable knowledge reserve level for innovation activities. From the evolution of knowledge network density, except for a few years, there is a steady downward trend from 2000 to 2017. With the increase in defense decryption patent cooperation, knowledge network density gradually decreased. From 2011 to 2015, network density remained relatively stable between 0.018 and 0.021. However, due to data changes, network density increased to 0.043 in 2016–2017. This indicates that with the increase in application quantity, the involved knowledge fields became more extensive, but the connections between knowledge elements became sparser, providing more possibilities for knowledge element combinations. Regarding the evolution of network centrality, from 2000 to 2017, there was a steady upward trend. Before 2010, network centrality grew slowly, with an average degree of less than 2, indicating that a piece of knowledge usually only had connections with fewer than two other pieces of knowledge. Entering the second decade of the 21st century, network centrality increased significantly. From 2010 to 2015, knowledge network centrality exceeded 3, and the average weighted degree exceeded 4, but it decreased in 2017. This indicates that knowledge elements in the innovative knowledge network of defense decryption patents are generally interconnected, showing great combination potential and providing more opportunities for knowledge element recombination. The diameter and average path length of the knowledge network showed a trend of first increasing and then decreasing from 2000 to 2017. In the first decade of the 21st century, as more new knowledge joined, the network expanded, increasing the diameter and path length. In the second decade, although new knowledge continued to join, cooperation between different knowledge fields became smoother, reducing the diameter and path length. This also created more opportunities for combining different knowledge elements. Table 5 Overall Indicators of National Defense Decryption Patent Knowledge Network Year Number of Nodes Average Degree Average Weighted Degree Network Density Network Diameter Average Path Length 2001 4 1.000 1.000 0.333 1 1.000 2003 2 1.000 1.000 1.000 1 1.000 2005 39 1.333 2.051 0.035 3 1.410 2007 40 1.550 2.400 0.040 7 2.402 2009 39 1.487 1.949 0.039 3 1.381 2011 151 3.099 4.477 0.021 11 4.236 2013 206 3.748 5.311 0.018 8 3.632 2015 168 3.381 4.833 0.020 9 3.620 2017 47 1.957 3.404 0.043 5 2.092 5.5 Evolution of Individual Networks in National Defense Decryption Patent Knowledge Using Gephi 0.10.1, this paper calculated the star nodes of the national defense decryption patent knowledge network for each research sample period, as shown in Table 6 . The results show that not all knowledge fields are prominent, and the top five innovative entities in each period mainly distribute their patents in four fields: B (operations, transportation), C (chemistry, metallurgy), G (physics), and H (electricity). This indicates that chemistry, physics, and electricity remain the primary fields of innovative activities for defense decryption patents, while patents in other fields such as human necessities, textiles, and construction remain at lower cooperation levels. Table 6 National Defense Decryption Patent Knowledge Network Star Nodes 2005 2007 2009 2011 2013 2015 2017 Node Degree Node Degree Node Degree Degree Node Degree Node Degree Node Degree G01C 3 (7) C22C 4 (7) F42B 3 (3) B64G 16 (19) B64G 41 (59) G06F 27 (44) H02K 5 (5) H04B 3 (3) G05B 4 (7) G01C 3 (3) G02B 13 (16) G01M 23 (33) B64G 22 (43) C08L 4 (18) G06K 2 (6) B64D 3 (4) H04B 3 (3) B64C 12 (20) F42B 21 (27) G01M 18 (29) G06F 4 (5) G06T 2 (6) F41H 3 (4) C22C 2 (5) F42B 11 (20) G01N 18 (23) F41H 16 (24) G01S 4 (4) G03B 2 (4) B21J 3 (3) H01Q 2 (3) G01S 11 (16) G02B 16 (22) F42B 15 (18) C08F 3 (16) (Note: Numbers in parentheses indicate weighted degree. When degree ranking is tied, weighted degree is used for further ranking.) 6 Discussion and Implications 6.1 Discussion 6.1.1 Dynamic Evolution of the Cooperative Network of Defense Decryption Patents This study applied social network analysis (SNA) to examine the structural evolution of the defense decryption patent cooperation network. The results indicate that with an increasing number of cooperating entities, the network has shifted from a single-entity collaboration model to a multi-entity cooperation model. The increasing complexity of cooperative relationships has led to a denser and more interconnected network, accelerating the flow of innovation, knowledge exchange, and technology transfer. Despite this growth, network density and average degree remain relatively low, signifying that direct interactions among entities are still limited. This suggests that while core institutions are strengthening their cooperation, peripheral entities still face challenges in accessing the network. Encouraging broader collaboration, particularly with small and medium-sized enterprises (SMEs) and universities, could enhance cross-sector knowledge transfer and innovation diffusion. 6.1.2 Stable Cooperation Relationships of Star Nodes in the Defense Decryption Patent Cooperation Network Using centrality measures, the study identified star nodes as key entities that play a leading role in patent cooperation. These nodes—primarily military enterprises, research institutions, and defense academies—demonstrate high degree and betweenness centrality, allowing them to act as knowledge hubs. Their stable cooperative relationships enable them to retain critical expertise and guide the development of defense-related technologies. The results highlight that star nodes attract collaborations due to their established resources and influence, reinforcing their central position in the network. However, this preferential attachment effect also suggests that new entrants may struggle to integrate into the network, potentially limiting the diversity of innovation. To address this, policies should encourage star nodes to mentor and collaborate with emerging innovators, ensuring that knowledge diffusion is not restricted to a select few institutions. 6.1.3 General Interconnection Among Knowledge Elements in Defense Decryption Patent Innovation By analyzing the knowledge network structure, the study found that the network diameter and average path length have gradually decreased, meaning that knowledge elements are becoming more interconnected. This increased interconnection indicates greater opportunities for recombining knowledge elements across different fields, fostering the emergence of dual-use innovations applicable to both military and civilian sectors. The findings suggest that defense decryption patents remain concentrated in high-tech fields such as materials science, electronics, and precision manufacturing, while traditional industries, including textiles and construction, show lower integration. Expanding the applicability of defense patents to civilian industries could generate broader societal and economic benefits. Establishing cross-industry partnerships can promote the diffusion of high-value defense innovations into non-military domains, such as healthcare, energy, and infrastructure. 6.2 Implications 6.2.1 Strengthening the Defense Decryption Patent Cooperation Ecosystem To fully leverage the potential of defense decryption patents, it is essential to establish a robust cooperation ecosystem that facilitates collaboration between military, private enterprises, universities, and research institutions. This collaboration should aim to break down traditional barriers between defense and civilian sectors, allowing for seamless knowledge exchange and technology transfer. By fostering an interconnected network of innovation-driven entities, the commercialization of defense technologies can be significantly accelerated. One of the primary strategies to achieve this is by enhancing cross-sector collaboration through dedicated government-backed initiatives that connect defense research institutes with civilian industries. Special programs can be designed to encourage joint research projects, co-development programs, and innovation partnerships, ensuring that military-originated technologies find relevant applications in civilian markets. Moreover, the government should provide strong financial incentives to facilitate technology transfer. This can include tax reductions, direct subsidies, R&D grants, and patent commercialization funds to encourage firms to participate in defense-related innovation. Many defense technologies have high development and adaptation costs, making it difficult for private enterprises—especially SMEs and startups—to engage in technology transfer without financial support. Creating dedicated defense patent transformation funds could help mitigate these barriers and promote innovation. Another critical step is improving patent accessibility by establishing open-access national defense patent databases. Such databases should be designed to provide real-time information on declassified defense patents, allowing civilian companies to identify and explore potential applications. A well-organized defense patent repository, along with clear technology licensing guidelines, would streamline commercialization and attract a wider range of industry players. Finally, to ensure efficient commercialization, governments should develop a streamlined regulatory framework that reduces bureaucratic obstacles associated with technology licensing, patent approvals, and military-civilian technology transfers. Simplified procedures would allow companies to quickly access and integrate military innovations into commercial applications, thus shortening the time from patent declassification to market adoption. 6.2.2 Supporting Star Institutions as Key Innovation Hubs Given the central role of star institutions in driving innovation, targeted policies and funding should be directed toward supporting these key entities. Star institutions—including leading defense research centers, military universities, and defense-oriented enterprises—serve as hubs that connect various entities within the innovation network. Their strategic positioning, extensive R&D capabilities, and access to critical resources make them well-suited to lead collaborative efforts in defense technology commercialization. One effective strategy is to encourage the formation of technology clusters, where star institutions, universities, private firms, and defense research centers co-locate in dedicated innovation zones. These clusters should be supported by government grants, preferential policies, and shared infrastructure, ensuring that all participants can benefit from proximity and interdisciplinary collaboration. Successful examples of defense innovation hubs worldwide—such as the U.S. Defense Innovation Unit (DIU) and Germany’s Fraunhofer Institutes—demonstrate that multi-entity clusters significantly accelerate defense technology transitions. To further enhance collaboration, co-development programs should be created to actively involve SMEs and startups in the defense innovation ecosystem. Many of these smaller entities possess specialized technological capabilities but lack direct access to military-related projects. By establishing mentorship programs, joint R&D initiatives, and technology incubators, star institutions can facilitate knowledge sharing and help commercialize emerging technologies. In addition to fostering short-term collaborations, governments should establish long-term R&D funding schemes aimed at sustaining innovation ecosystems. These funding schemes should not only support major defense institutions but also allocate resources for high-potential smaller players, ensuring that a diverse set of contributors drive national defense innovation forward. Moreover, policymakers should introduce regulatory flexibility that allows star institutions to engage in commercial partnerships more effectively. Currently, many defense institutions face regulatory restrictions when collaborating with civilian enterprises. Reforming these policies and streamlining approval processes for military-civilian technology partnerships can enable greater cross-sector integration, leading to faster and more impactful defense technology adoption. 6.2.3 Promoting the Marketization of Defense Decryption Patents To ensure that defense-originated innovations reach the commercial market, the government must establish a systematic approach for promoting the marketization of decryption patents. Currently, many high-potential military technologies remain underutilized due to a lack of industry awareness, complex licensing procedures, and insufficient commercialization channels. Addressing these challenges requires proactive government intervention, industry participation, and strategic investment. A crucial first step is to develop technology matchmaking platforms where defense patent holders can connect with private-sector enterprises seeking innovative solutions. These platforms should provide detailed technology descriptions, potential application areas, licensing terms, and market feasibility assessments to help companies identify relevant patents. Additionally, regular industry-defense innovation forums, technology expos, and matchmaking events can further strengthen direct engagement between military researchers and private-sector investors. Another essential measure is facilitating public-private partnerships (PPPs) that leverage government resources and private-sector expertise to scale military innovations for broader use. In particular, venture capital funds and startup accelerators focused on dual-use technologies should be established to help innovative firms commercialize defense patents. By providing targeted financial incentives and risk-sharing mechanisms, these partnerships can reduce commercialization costs and encourage broader market participation. Governments should also study international best practices and adopt proven commercialization strategies from leading defense innovation ecosystems. Countries such as the United States, Israel, and South Korea have successfully transitioned military technologies into thriving civilian industries. By analyzing how these nations have structured their defense-to-civilian commercialization pipelines, policymakers can develop China-specific strategies that align with the country’s industrial policies and innovation ecosystem. Finally, defense decryption patents should be integrated into national economic planning, ensuring that defense-driven innovations contribute to long-term economic growth. Strategic sectors such as aerospace, cybersecurity, medical technology, and advanced manufacturing can greatly benefit from military-derived R&D, creating a high-tech, innovation-driven economy. Declarations Ethics approval and consent to participate Not applicable. This study analyzed declassified patent records and did not involve human participants, human data, or animals. Funding This work was supported by the Innovation Team Project for Ordinary Universities in Guangdong Province (Humanities and Social Sciences) (Grant No. 2023WCXTD026) and Major Scientific Research Projects in Colleges and Universities in Guangdong (Grant No. 2021ZDJS122)(Grant No. 2022ZDJS142).Guangdong Province Philosophy and Social Science Planning Project (GD23YGL28)(GD24CGL26)Guangdong Provincial Society of Logistics and Supply Chain Project (2023LS017C) Author Contribution H.L. conceived and designed the study, collected and curated the patent data, performed the network construction and statistical analyses, interpreted the results, and wrote the main manuscript text. H.L. prepared all figures and tables. H.L. reviewed and approved the final manuscript. Data Availability All data supporting the findings of this study are included in this published article and its Supplementary Information files. The record-level patent dataset compiled for this study (including patent identifiers and cleaned bibliographic metadata) are provided as Supplementary Data. References Abbasi, A., Hossain, L. & Leydesdorff, L. Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. J. Informetrics . 6 (3), 403–412. https://doi.org/10.1016/j.joi.2012.01.002 (2012). Acosta, M., Coronado, D., Ferrándiz, E. & León, M. D. Civil-Military Technology Transfer and Economic Performance: An Analysis of Spanish Defense Patents. Def. Peace Econ. 31 (6), 647–666 (2020). Acosta, M., Coronado, D., Ferrándiz, E., Marin, M. R. & Moreno, P. J. Civil–Military Patents and Technological Knowledge Flows Into the Leading Defense Firms. J. De?F. Manage. 57 (4), 112–132. https://dx.doi.org/10.1177/0095327X18823823 (2020). 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Proceedings of the International Conference on Social Science, Public Health and Education, 74, 331–334. (2021). https://dx.doi.org/10.2991/ASSEHR.K.210519.074 Additional Declarations No competing interests reported. Supplementary Files data0123.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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safeguarding technological innovations, playing a vital role in economic transformation and sustainable development. Unlike ordinary patents, defense patents are closely linked to national security and are often classified to prevent unauthorized access. However, declassifying and commercializing these patents can significantly enhance their economic and societal value. In recent years, several countries have leveraged patent transfer mechanisms to promote the transformation of defense technologies into civilian applications (Acosta et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, the United States implemented the Bayh-Dole Act, which allows private entities to retain ownership of patents developed through federally funded research, thus fostering innovation and technology transfer (Mowery et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Similarly, South Korea has adopted civil-military integration policies to enhance national defense capabilities while stimulating economic growth (Drifte, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn China, the Military-Civil Fusion (MCF) strategy has been elevated to a national priority, aiming to integrate military and civilian sectors for innovation and technological advancement (Kania, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Chinese government has actively promoted the declassification of defense patents to facilitate their commercialization. As of June 2023, a total of 7,656 defense patents have been declassified, reflecting the country's commitment to enhancing the transfer of military technologies to the civilian sector (Agranat \u0026amp; Marom, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, despite these initiatives, several challenges remain. The lack of systematic research on the structural evolution of defense decryption patent collaboration networks, the changing roles of key innovation entities, and the interconnections within defense patent knowledge networks limit our understanding of how these patents contribute to technological progress and industrial development.\u003c/p\u003e \u003cp\u003eExisting studies suggest that military-civilian patent collaboration networks play a crucial role in accelerating defense technology transfer (Sullivan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Through strategic partnerships, enterprises gain access to a broader pool of resources, fostering industry-university-research collaborations that optimize the innovation ecosystem (Hu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu \u0026amp; Xu, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there remains a gap in understanding how defense decryption patent cooperation networks evolve over time, which entities play central roles in these networks, and how knowledge elements interact within the system. Addressing these gaps is essential for maximizing the impact of declassified defense patents on national innovation capabilities.\u003c/p\u003e \u003cp\u003eThis study aims to analyze the basic characteristics of declassified defense patents, investigate the dynamic evolution of their cooperation networks using social network analysis, and explore the development of their knowledge networks. By integrating individual networks, cooperative networks, and knowledge network evolution, this research provides a comprehensive understanding of the collaboration and knowledge dynamics within defense decryption patents. The findings offer valuable insights for policymakers and industry stakeholders to optimize the allocation of innovation resources and enhance civil-military integration strategies.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a literature review on defense patent collaboration and knowledge networks. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the data sources and methodologies employed in this study. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e analyzes the dynamic evolution of defense decryption patent cooperation networks, while Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5\u003c/span\u003e explores the development of their knowledge networks. Section \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003e6\u003c/span\u003e discusses the implications of the findings and provides recommendations for future research and policy formulation.\u003c/p\u003e \u003cp\u003eThe declassification and subsequent integration of defense patents into civilian industries have been pivotal in advancing technological innovation and economic development across various nations. In March 2017, China declassified over 3,000 national defense patents, marking a significant move to promote civil-military integration (CMI) and enhance civilian participation in defense technology (International Institute for Strategic Studies, 2018). This initiative aimed to bridge the gap between military advancements and civilian applications, fostering a symbiotic relationship between the two sectors.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003eThe dynamic evolution of collaborative innovation networks is central to understanding technological progress. These networks, comprising enterprises, universities, research institutions, government entities, and social service systems, evolve through changes in relationships and resource exchanges among participants. Factors such as complementary resources, member changes, and preferential link mechanisms drive this evolution, leading to structural transformations within the network (Zeng et al., 2019). In the context of defense technology, the declassification of patents has introduced new actors into the innovation network, thereby altering its dynamics and fostering novel collaborations.\u003c/p\u003e \u003cp\u003eKnowledge networks, which represent the intricate web of knowledge elements and their interconnections, play a crucial role in the innovation process. The system of scientific innovation can be characterized as a complex, multi-layered network of actors, their products, and knowledge elements. Despite the progress that has been made, a more comprehensive understanding of the interactions and dynamics of this multi-layered network remains a significant challenge (Frontiers in Physics, 2024). In the realm of defense patents, the integration of declassified knowledge into civilian industries necessitates a thorough understanding of these knowledge networks to effectively harness and build upon existing innovations.\u003c/p\u003e \u003cp\u003eThe interplay between collaborative networks and knowledge networks is evident in the concept of embeddedness, which examines how the position of entities within a network influences their collaborative behaviors and innovation outcomes. Research indicates that both the collaborative interactions of organizations and their knowledge element exchanges significantly impact the dynamic changes of collaborative innovation networks. By analyzing multiple collaboration and knowledge networks, studies have revealed how social and knowledge network embeddedness affects collaboration dynamics (Chen et al., 2023). This perspective is particularly relevant in the context of declassified defense patents, where the integration of new knowledge elements into existing networks can lead to the emergence of innovative collaborations and technological advancements.\u003c/p\u003e \u003cp\u003eIn summary, the declassification of defense patents serves as a catalyst for the dynamic evolution of collaborative innovation and knowledge networks. Understanding the mechanisms underlying these evolutions is essential for effectively leveraging declassified knowledge to drive technological innovation and economic growth.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Source\u003c/h2\u003e \u003cp\u003ePatent records were retrieved from the Wisdom Bud (ZhiHuiYa) patent database. Access to Wisdom Bud was provided through a licensed institutional subscription held by Guangzhou College of Technology and Business. We queried declassified Chinese national defense patents (\u0026ldquo;defense decryption patents\u0026rdquo;) published between 1992 and 2017.\u003c/p\u003e \u003cp\u003eThe search strategy combined keywords, International Patent Classification (IPC) codes, and applicant/inventor fields. Bibliographic and technical metadata were exported, including patent title/abstract, application and publication numbers, applicants, inventors, filing/publication dates, and IPC codes.\u003c/p\u003e \u003cp\u003eData cleaning was conducted to ensure consistency and suitability for network construction: duplicate records were removed; patents with missing key metadata (e.g., applicants/inventors, IPC codes, or year information) were excluded; and records outside the 1992\u0026ndash;2017 time window were removed. For the cooperation-network analyses, we further retained patents involving at least two collaborating entities, yielding 728 cooperative patents.\u003c/p\u003e \u003cp\u003ePermissions and ethics. Wisdom Bud is a third-party database; the authors had permission to access and analyze the data for research purposes under the database license/terms of use. The study relies on declassified patent records and does not involve human participants, human data, or animals; therefore, ethics approval and informed consent were not required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Variables and Metrics Calculation\u003c/h2\u003e \u003cp\u003eIn social network analysis (SNA), the fundamental elements are nodes and edges. In this study, nodes represent patent applicants or inventors, while edges denote the collaborative relationships between them. To analyze the structure and characteristics of the collaboration network, we calculated several key metrics. Degree Centrality measures the number of direct connections a node has, indicating its activity level within the network. Weighted Degree Centrality accounts for the strength of connections by assigning weights to edges, providing a more nuanced view of a node's importance. Clustering Coefficient assesses the extent to which a node's neighbors are interconnected, reflecting the tendency of nodes to form tightly knit groups. Average Path Length calculates the mean number of steps along the shortest paths for all possible pairs of network nodes, offering insights into the efficiency of information or resource flow within the network. Network Density represents the ratio of actual connections to all possible connections in the network, highlighting the overall level of connectivity. Collectively, these metrics provide a comprehensive understanding of the network\u0026rsquo;s topology and the roles of individual entities within it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis Method\u003c/h2\u003e \u003cp\u003eWe employed social network analysis techniques to examine the collaborative structures among entities involved in declassified defense patents. Gephi, an open-source network analysis and visualization tool, was utilized for this purpose. Gephi supports various network metrics calculations and provides dynamic visualization capabilities, making it suitable for analyzing complex networks (Gephi, n.d.).\u003c/p\u003e \u003cp\u003eThe analysis process involved importing the cleaned dataset into Gephi, where nodes and edges were defined based on the relationships among patent applicants and inventors. Subsequently, the aforementioned metrics were computed to evaluate the network's properties. Visualizations were generated to illustrate the network's structure, highlighting key nodes and clusters that play pivotal roles in the collaborative landscape of declassified defense patents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 China\u0026rsquo;s Defense Patent System and Key Legal Regulations\u003c/h2\u003e \u003cp\u003eChina\u0026rsquo;s defense patent system is designed to protect intellectual property related to national security while promoting the commercialization of defense technologies. The foundation of this system was established with the Regulations on National Defense Patents (1990), which define defense patents as those involving national defense interests and requiring confidentiality (State Council, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Under these regulations, the National Defense Intellectual Property Office oversees the review and approval process for defense patents, ensuring compliance with security protocols and technological development goals.\u003c/p\u003e \u003cp\u003eTo strengthen intellectual property management in defense research and development (R\u0026amp;D), the National Intellectual Property Strategy Outline (2008) introduced a coordinated mechanism to regulate defense patent ownership, licensing, and incentives (State Council, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This policy aimed to integrate intellectual property management into national defense R\u0026amp;D, manufacturing, and procurement processes, enhancing the utilization of military technologies in civilian industries.\u003c/p\u003e \u003cp\u003eMore recently, China\u0026rsquo;s 14th Five-Year Plan for Intellectual Property Protection and Utilization (2021) further emphasized the modernization of defense-related IP regulations. It called for an improved legal framework to facilitate technology transfer and commercialization while maintaining national security safeguards (State Council, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The policy highlights the need for effective cooperation between military and civilian sectors, ensuring that declassified defense patents can contribute to broader technological advancements.\u003c/p\u003e \u003cp\u003eThrough these progressive legal and policy measures, China has established a structured framework that balances national security interests with innovation-driven military-civil integration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Temporal Characteristics\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Cooperation Quantity and Node Temporal Characteristics\u003c/h2\u003e \u003cp\u003eDue to the confidentiality of the discipline, relevant research invention patents are mainly self-applied, and the total number of defense decryption cooperative patents is relatively small. Specifically, from 1992 to 2017, the total number of defense decryption cooperative patents was 728. Among them, the total number of defense decryption cooperative patents from 1992 to 1999 was 44, accounting for only 6.04% of the total; from 2000 to 2009, the total number of defense decryption cooperative patents was 66, accounting for only 9.07% of the total; and from 2010 to 2017, the total number of defense decryption cooperative patents was 618, accounting for 83.79% of the total.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe above conclusions are corroborated by Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Before 2000, the number of defense decryption cooperative patents was relatively low, with the number of applications and authorized defense decryption cooperative patents not exceeding 25 per year, showing a relatively flat and minor fluctuating curve. The main reasons are that the second revision of the \u0026ldquo;Patent Law\u0026rdquo; was conducted in 2000, and before this, patent policies were not sufficiently perfected, and patent cooperation did not receive enough attention from innovation entities. The statistical results show that before 2000, the network structure of defense decryption cooperative patents was loose, with few innovative entities participating in cooperation, and cooperation between innovative entities was relatively singular. After 2000, the number of defense decryption cooperative patents increased rapidly due to the continuous development of technology and science, where single entities could not meet practical needs, and when the knowledge and technology foundation was insufficient to support existing R\u0026amp;D needs, entities turned to seek more cooperative partners, leading to increased organizational cooperation. Since recent years' patents have not been fully authorized, the number of patents has dramatically decreased. Therefore, to ensure the accuracy of the cooperative network evolution analysis, this paper only selects the years after 2000 for analysis, with the research sample being from 2000 to 2017. Given the overall low number of defense decryption cooperative patents, each research sample interval is analyzed every two years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e1992\u0026ndash;2017 number of national defense decryption cooperation patents and number of participating cooperation nodes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Patents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Nodes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Temporal Characteristics of Core Patent Applicants\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a heat map of patent applications by the top ten patent applicants over the analyzed period. The Beijing Institute of Technology maintained steady patent activity throughout all time periods, while the Xi\u0026rsquo;an Institute of Modern Chemistry exhibited a sharp surge between 2012 and 2017, reaching 208 patents, making it the most active applicant. Northwestern Polytechnical University and the China Air-to-Air Missile Research Institute showed significant growth after 2002, particularly between 2007 and 2012, while the China Academy of Launch Vehicle Technology saw gradual increases, peaking at 109 patents between 2012 and 2017. The Beijing Spacecraft General Design Department and Chinese National University of Defense Technology steadily increased their contributions after 2002, while the 54th Research Institute of China Electronics Technology Group Corporation grew significantly from 2007 onward. Notably, the Shanghai Institute of Aerospace Systems Engineering and the 23rd Research Institute of China Aerospace Science and Industry Corporation emerged as key contributors after 2007, reflecting an increasing focus on aerospace and defense technologies in recent years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Temporal Characteristics of IPC Patent Classification\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the temporal characteristics of IPC patent classifications from 1992 to 2017. Initially, C class (chemistry, metallurgy) had the most patents, which was later overtaken by F class (mechanical engineering), and recently, G class (physics) has had the most patents. This trend likely reflects shifts in technological focus and research directions. F (mechanical engineering), G (physics), and H (electricity) classes show significant growth trends, representing key areas of current and future research. An analysis of IPC classification numbers in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that the top ten most frequently appearing IPC classification codes mainly focus on measurement and testing (G01), computing technology (G06), and telecommunications technology (H04), indicating these fields are the current research or patent application priorities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Evolution of Cooperative Network Structure","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Overall Evolution of National Defense Decryption Patent Cooperation Network\u003c/h2\u003e \u003cp\u003eBased on the selected data of defense decryption cooperative patents from 2000 to 2017, the national defense decryption patent cooperation network is constructed with each two-year period as a sample interval. Each subject involved in cooperation is taken as source and target nodes, and their connections are taken as edges to construct the cooperation network. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the dynamic evolution process of the national defense decryption cooperation network from 2000 to 2017, with larger nodes indicating more entities cooperating with the node and thicker edges indicating more frequent cooperation between the nodes. Overall, the national defense decryption patent cooperation network is continually growing and developing, with an increasing number of entities participating in cooperation and strengthening relationships among entities, making the network more complete. Specifically, in the network structure from 2001 to 2010, the number of innovative entities participating in cooperation increased year by year, and their relationships became more intricate, playing a good transitional role over these ten years. In the 2010s, the national defense decryption patent cooperation network became more complex, with an increasing number of innovative entities participating in cooperation and more complex cooperation relationships, with some entities cooperating with multiple others, becoming \"bridges\" in the cooperation network. This promotes indirect cooperation between other entities, pushing for \"multi-entity-multi-entity\" collaboration, thereby expanding the scope of defense decryption patent cooperative innovation. The most representative entities are the \"China Academy of Launch Vehicle Technology\" and \"Beijing Institute of Technology,\" as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn line with the research theme of this paper, the social network indicators used are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicator Selection and Explanation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeaning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Network Nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of entities participating in the network.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Network Edges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of connections between nodes, representing the breadth of cooperation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Network Connections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of connections between nodes, representing the depth of cooperation. For example, if A and B have cooperated four times, their number of edges is 1, and the number of connections is 4.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of other entities a node cooperates with.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted Network Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe total number of cooperation instances a node has.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average degree of the network, representing the breadth of cooperation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Weighted Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average weighted degree of the network, representing the depth of cooperation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe ratio of actual to potential edges in the network, indirectly representing the scale of cooperation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe maximum shortest path length between any two nodes in the network.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Path Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average shortest path length between any two nodes, indicating ease of connection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing Gephi 0.10.1 software, the overall structural indicator values of the national defense decryption patent cooperation network were calculated, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall Indicators of National Defense Decryption Patent Cooperation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Nodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Edges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Connections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage Weighted Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNetwork Diameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAverage Path Length\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe number of nodes measures the network scale, indicating the number of entities participating in the network. From the network scale (total nodes), except for a few years, the number of nodes has increased annually, reaching 118 in 2017, indicating a yearly increase in the number of participating entities.\u003c/p\u003e \u003cp\u003eThe number of edges represents the number of connections between nodes, and the number of connections represents the number of connections between nodes, indicating the depth of cooperation. For instance, if A and B have cooperated four times, their number of edges is 1, and the number of connections is 4. Combining Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the number of edges has also increased annually, except for a few years, indicating an increase in the actual connections in the network. The connections increased from 4 in the first phase to 77, indicating more diversified cooperation modes, evolving from \"single entity-single entity\" cooperation to \"multi-entity-multi-entity\" cooperation (see Fig.\u0026nbsp;6). Comparing the number of edges and connections reveals that before 2010, the connections and edges were similar, with many years having equal connections and edges, indicating that before 2010, each entity generally only cooperated once with its partner, without regular cooperation. After 2010, the difference between connections and edges gradually increased, indicating that cooperation between entities and fixed partners increased, reaching a relatively stable cooperation state.\u003c/p\u003e \u003cp\u003eAverage degree represents the average number of connections each node has in the network. A higher average degree means more nodes are connected to each node in the network. Average weighted degree reflects the average connection strength based on the number of connections, with higher values indicating more frequent connections between nodes. Overall, the average degree remained between 1 and 1.667, indicating that each node had less than two connections on average. Except for a few years, the average weighted degree remained below 2, indicating a maximum average connection of two between nodes. In 2016\u0026ndash;2017, the average weighted degree reached a maximum of 3.847, indicating a maximum average connection of nearly four during this period. This suggests that the depth of cooperation in the defense decryption patent cooperation network still needs to be strengthened, promoting more cooperation between different entities.\u003c/p\u003e \u003cp\u003eNetwork density describes the extent of interconnection among nodes. Overall, network density showed a downward trend, as an increase in the number of nodes and the breadth of connections led to a decrease in density. Specifically, from 2000 to 2007, network density was greater than 0.1, fluctuating between 0.143 and 0.333. After 2008, network density fell below 0.1, maintaining a relatively stable level between 0.041 and 0.069 from 2008 to 2015, and reaching the lowest value of 0.011 in 2016\u0026ndash;2017 due to increased network breadth and potential connections. Network density results indicate that although cooperation relationships in the defense decryption patent cooperation network have improved, the current connection strength and breadth are insufficient to achieve a fully connected network. Thus, defense decryption patent cooperation needs to expand cooperation breadth further while maintaining depth, promoting knowledge sharing among multiple entities, and accelerating the integration of heterogeneous capital.\u003c/p\u003e \u003cp\u003eNetwork diameter refers to the maximum shortest path length between any two nodes, and average path length indicates the average shortest path length between any two nodes. As network density decreases, network diameter and average path length increase, indicating greater effort and longer paths to establish connections between nodes. This also means that information can be transmitted over greater distances, promoting knowledge sharing among different entities and accelerating the integration of heterogeneous resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Evolution of Individual Networks in National Defense Decryption Patent Cooperation\u003c/h2\u003e \u003cp\u003eIn the national defense decryption patent cooperation network, \u003cb\u003estar nodes\u003c/b\u003e refer to central nodes that receive significant attention, similar to \"stars\" in a particular field. These nodes exhibit high innovation performance, are more visible to other nodes due to their strategic positions, and are preferred partners in new cooperation opportunities. To identify and analyze these star nodes, this study employs several \u003cb\u003ecentrality indicators\u003c/b\u003e that measure different aspects of a node's influence within the network.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDegree centrality\u003c/b\u003e measures the number of direct connections a node has, indicating its level of activity within the network. A higher degree centrality suggests that the node collaborates with more entities, making it a key player in the cooperation network. It is calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{C}_{D}\\left(i\\right)={k}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003erepresents the degree centrality of node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the number of direct connections that node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e has.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBetweenness centrality\u003c/b\u003e quantifies the extent to which a node acts as a bridge between other nodes, controlling the flow of information and collaboration opportunities. A high betweenness centrality indicates that the node plays a crucial intermediary role in connecting different parts of the network. It is defined as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{C}_{B}\\left(i\\right)={\\sum\\:}_{s\\ne\\:i\\ne\\:t}\\frac{{\\sigma\\:}_{st}\\left(i\\right)}{{\\sigma\\:}_{st}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{st}\\)\u003c/span\u003e\u003c/span\u003e is the number of shortest paths between nodes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{st}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e is the number of those paths passing through node ii.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCloseness centrality\u003c/b\u003e measures how efficiently a node can reach all other nodes in the network. A node with high closeness centrality is well-positioned to access and disseminate information quickly. It is calculated as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{C}_{C}\\left(i\\right)=\\frac{1}{{\\sum\\:}_{j}d\\left(i,j\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\left(i,j\\right)\\)\u003c/span\u003e\u003c/span\u003e is the shortest path distance between node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEigenvector centrality\u003c/b\u003e assesses a node\u0026rsquo;s importance based on its connections to other well-connected nodes. Unlike degree centrality, which considers only direct connections, eigenvector centrality accounts for the overall influence of a node within the entire network. It is computed by solving the eigenvector equation:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{A}\\mathbf{x}=\\lambda\\:\\mathbf{x}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{A}\\)\u003c/span\u003e\u003c/span\u003e is the adjacency matrix of the network, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e is the largest eigenvalue, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{x}\\)\u003c/span\u003e\u003c/span\u003e is the eigenvector whose components indicate the centrality scores of the corresponding nodes.\u003c/p\u003e \u003cp\u003eIn this study, we calculate these centrality measures for each node and define the top five nodes with the highest scores in each period as \u003cb\u003estar nodes\u003c/b\u003e. By analyzing their characteristics and temporal evolution, we gain valuable insights into the role of key innovation entities in the national defense decryption patent cooperation network and their impact on the network\u0026rsquo;s development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNational Defense Decryption Patent Cooperation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA Chongqing Garrison District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeijing Institute of Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePLA Guangzhou Military Region Engineering Research and Design Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing North Dahua Technology Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLA General Staff Department Fifth Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChinese Academy of Sciences National Astronomical Observatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA Armored Engineering Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina Electronics Equipment System Engineering Corporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHangzhou Institute of Electronic Industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEIC Asset Management Limited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWuhan Institute of Physics and Mathematics, Chinese Academy of Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDalian Naval Academy of the Chinese People's Liberation Army Navy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangzhou Military Region Engineering Research and Design Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnhui Hongxing Machinery Factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina North Industries Group Corporation Qinhuangdao Yao Glass Fiber Reinforced Plastic Factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Time Service Center, Chinese Academy of Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorthwestern Polytechnical University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwestern Polytechnical University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese Academy of Sciences National Astronomical Observatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLA Unit 92537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShenyang Aircraft Corporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthwestern Polytechnical University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRizhao Hatton Microarc Oxidation Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHarbin Institute of Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaoji Rare Metals Equipment Design Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanda Electronics Group Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaosteel Special Steel Division\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing Institute of Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNanjing Panda Electronics Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeijing University of Aeronautics and Astronautics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthwestern Polytechnical University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina Electronics Equipment System Engineering Corporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina Academy of Launch Vehicle Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(124)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaosteel Special Steel Division\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeijing Precision Mechanical and Electrical Control Equipment Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeijing Institute of Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarbin Institute of Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina Academy of Launch Vehicle Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina North Industries Group Corporation 203 Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShenyang Aircraft Corporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina Shipbuilding Industry Corporation No. 704 Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXi'an Guidance Technology Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanda Electronics Group Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLA Naval Equipment Research Institute Ship Demonstration Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShanghai Jiao Tong University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e(Note: Numbers in parentheses indicate weighted degree. When two enterprises have the same degree ranking, the weighted degree is used for further ranking.)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDue to the limited cooperation patent data before 2000, this paper only calculates data after 2000. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, except for a few periods, the degree and weighted degree of star nodes in each period are relatively high, being the network leaders. Specifically, in 2000\u0026ndash;2001 and 2006\u0026ndash;2007, the degree of star nodes was 1, indicating inactive networks where star nodes had only one connection with another entity. In other periods, the degree of top-ranking star nodes was greater than 1, indicating cooperation with multiple partners. Overall, star nodes' positions have become increasingly prominent over time. Particularly in 2016\u0026ndash;2017, the degree of China Academy of Launch Vehicle Technology reached 8, and its weighted degree reached 124, while Beijing Institute of Technology's degree reached 6, ranking first and second among star nodes. This indicates their cooperation with various entities and stable cooperation with specific entities, such as China Academy of Launch Vehicle Technology's 58 collaborations with Beijing Aerospace Automatic Control Institute and 30 collaborations with Beijing Aerospace Long March Flying Vehicle Research Institute, representing typical multi-entity cooperation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Evolution of Knowledge Network in National Defense Decryption Patents","content":"\u003cp\u003eIn China, patent applications typically adopt the International Patent Classification (IPC) system to categorize technologies and identify technical domains. This study extracted the first four digits of IPC classification numbers to identify 648 distinct knowledge elements. Based on the relationships between these elements, we constructed a knowledge network for national defense decryption patents, employing Social Network Analysis (SNA) to analyze its structural properties.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Data Integration and Network Construction\u003c/h2\u003e \u003cp\u003eEach knowledge element can exist independently, indicating that it contributes to patents without forming direct technological collaborations. However, knowledge elements may also form knowledge combinations, represented by edges in the network, which signify technological convergence when two or more elements appear within the same patent. Furthermore, connections between knowledge elements and defense innovation entities indicate ownership relationships, showing which institutions possess specific technological capabilities.\u003c/p\u003e \u003cp\u003eThis approach aligns with patent network methodologies that utilize IPC-based mapping to study technological knowledge flow and innovation networks (Lee \u0026amp; Kim, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The co-occurrence of IPC codes within patents reflects the blending of distinct technologies, leading to interdisciplinary innovation and technology diffusion (Liu \u0026amp; Lu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Analytical Methods and Calculation Formulas\u003c/h2\u003e \u003cp\u003eTo quantify the structural properties of the knowledge network, we applied several key network analysis metrics commonly used in technological knowledge network studies (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These metrics help to identify the central knowledge elements, the degree of knowledge integration, and the role of defense entities in technological convergence.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Degree Centrality\u003c/h2\u003e \u003cp\u003eDegree centrality measures how well a knowledge element is connected within the network. A \u003cb\u003ehigher degree centrality\u003c/b\u003e indicates that the knowledge element frequently co-occurs with other elements, making it more influential in shaping technological advancements (Freeman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). The formula for \u003cb\u003edegree centrality\u003c/b\u003e is:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{C}_{D}\\left(i\\right)={k}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003erepresents the degree centrality of node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ is the number of edges connected to node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Betweenness Centrality\u003c/h2\u003e \u003cp\u003eBetweenness centrality assesses how often a knowledge element acts as a \u003cb\u003ebridge\u003c/b\u003e in the shortest paths between other elements. A \u003cb\u003ehigh betweenness centrality\u003c/b\u003e suggests that the element \u003cb\u003eplays a key role in linking different technological fields\u003c/b\u003e, facilitating knowledge transfer and innovation recombination (Brandes, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The formula is:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{C}_{B}\\left(i\\right)={\\sum\\:}_{s\\ne\\:i\\ne\\:t}\\frac{{\\sigma\\:}_{st}\\left(i\\right)}{{\\sigma\\:}_{st}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e​\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{st}\\)\u003c/span\u003e\u003c/span\u003e​ is the total number of shortest paths from node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\)\u003c/span\u003e\u003c/span\u003e to node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{st}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the number of those paths that pass through node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 Network Density\u003c/h2\u003e \u003cp\u003eNetwork density measures the \u003cb\u003edegree of interconnectivity\u003c/b\u003e within the knowledge network. A \u003cb\u003ehigher density value\u003c/b\u003e indicates a more interconnected technological landscape, which \u003cb\u003eaccelerates knowledge exchange and innovation diffusion\u003c/b\u003e (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The formula is:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:D=\\frac{2E}{N\\left(N-1\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:D\\)\u003c/span\u003e\u003c/span\u003e represents network density, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\)\u003c/span\u003e\u003c/span\u003e is the number of edges in the network, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e is the total number of nodes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Theoretical Basis and Literature Support\u003c/h2\u003e \u003cp\u003eThe knowledge recombination theory suggests that technological advancement occurs through the integration of diverse knowledge elements. IPC-based networks provide a valuable tool to map technological convergence and predict emerging innovations (Liu \u0026amp; Lu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous research has demonstrated that central knowledge elements in patent networks drive interdisciplinary collaboration and contribute to technological breakthroughs (Freeman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Lee \u0026amp; Kim, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe use of social network analysis in patent studies has been widely adopted to analyze innovation ecosystems, revealing how defense-related technologies evolve and integrate with civilian applications (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). By employing this methodology, our study provides a quantitative foundation for understanding how national defense decryption patents foster technological innovation and interdisciplinary knowledge exchange.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Overall Evolution of the Knowledge Network in National Defense Decryption Patents\u003c/h2\u003e \u003cp\u003eUsing Gephi 0.10.1 software, the overall knowledge network of national defense decryption patents from 2000 to 2017 was visualized, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFrom the figure, it is evident that there is communication and exchange between different technologies each year. Overall, the knowledge network of national defense decryption patents in China is continuously improving, with an increasing number of technological types participating in cooperation and strengthening relationships among technologies, making the network more complete. Entering the new century, with the second revision of the \"Patent Law,\" China's national defense decryption patent knowledge cooperation network continuously improved, with annual increases in cooperation among technological fields, especially after 2005. More technological fields participated in cooperation, making the knowledge network increasingly complex. China's national defense decryption patent knowledge cooperation network gradually transitioned from a \"single-knowledge-single-knowledge\" model to a \"multi-knowledge-multi-knowledge\" model, with representative knowledge fields being B64G and G01M, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGephi 0.10.1 software calculated overall indicators for each sample interval, showing that the utilization of knowledge in national defense decryption patents has steadily increased. Particularly after entering the 21st century, the knowledge network's scale expanded rapidly, peaking at 206 nodes in 2013, accounting for 31.79% of all knowledge elements. This indicates a rapid increase in the number of knowledge elements participating in patent applications as innovation entities delve deeper into new knowledge. However, less than half of the knowledge elements are still utilized in innovation activities, indicating significant potential for future growth. From 2016 to 2017, the knowledge network's scale decreased significantly, possibly due to some patent applications not yet being authorized, affecting the data. Overall, after 2011, the knowledge network's scale remained between 150 and 210, showing that defense innovation entities gradually found a suitable knowledge reserve level for innovation activities.\u003c/p\u003e \u003cp\u003eFrom the evolution of knowledge network density, except for a few years, there is a steady downward trend from 2000 to 2017. With the increase in defense decryption patent cooperation, knowledge network density gradually decreased. From 2011 to 2015, network density remained relatively stable between 0.018 and 0.021. However, due to data changes, network density increased to 0.043 in 2016\u0026ndash;2017. This indicates that with the increase in application quantity, the involved knowledge fields became more extensive, but the connections between knowledge elements became sparser, providing more possibilities for knowledge element combinations.\u003c/p\u003e \u003cp\u003eRegarding the evolution of network centrality, from 2000 to 2017, there was a steady upward trend. Before 2010, network centrality grew slowly, with an average degree of less than 2, indicating that a piece of knowledge usually only had connections with fewer than two other pieces of knowledge. Entering the second decade of the 21st century, network centrality increased significantly. From 2010 to 2015, knowledge network centrality exceeded 3, and the average weighted degree exceeded 4, but it decreased in 2017. This indicates that knowledge elements in the innovative knowledge network of defense decryption patents are generally interconnected, showing great combination potential and providing more opportunities for knowledge element recombination.\u003c/p\u003e \u003cp\u003eThe diameter and average path length of the knowledge network showed a trend of first increasing and then decreasing from 2000 to 2017. In the first decade of the 21st century, as more new knowledge joined, the network expanded, increasing the diameter and path length. In the second decade, although new knowledge continued to join, cooperation between different knowledge fields became smoother, reducing the diameter and path length. This also created more opportunities for combining different knowledge elements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall Indicators of National Defense Decryption Patent Knowledge Network\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Nodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage Weighted Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNetwork Diameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage Path Length\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Evolution of Individual Networks in National Defense Decryption Patent Knowledge\u003c/h2\u003e \u003cp\u003eUsing Gephi 0.10.1, this paper calculated the star nodes of the national defense decryption patent knowledge network for each research sample period, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results show that not all knowledge fields are prominent, and the top five innovative entities in each period mainly distribute their patents in four fields: B (operations, transportation), C (chemistry, metallurgy), G (physics), and H (electricity). This indicates that chemistry, physics, and electricity remain the primary fields of innovative activities for defense decryption patents, while patents in other fields such as human necessities, textiles, and construction remain at lower cooperation levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNational Defense Decryption Patent Knowledge Network Star Nodes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG01C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC22C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF42B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB64G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB64G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41\u003c/p\u003e \u003cp\u003e(59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eG06F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eH02K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH04B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG05B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG01C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG02B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG01M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB64G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eC08L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG06K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB64D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH04B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB64C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF42B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eG01M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eG06F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG06T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF41H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC22C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF42B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG01N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF41H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eG01S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG03B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB21J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH01Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG01S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG02B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF42B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eC08F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e(Note: Numbers in parentheses indicate weighted degree. When degree ranking is tied, weighted degree is used for further ranking.)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6 Discussion and Implications","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Discussion\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e6.1.1 Dynamic Evolution of the Cooperative Network of Defense Decryption Patents\u003c/h2\u003e \u003cp\u003eThis study applied social network analysis (SNA) to examine the structural evolution of the defense decryption patent cooperation network. The results indicate that with an increasing number of cooperating entities, the network has shifted from a single-entity collaboration model to a multi-entity cooperation model. The increasing complexity of cooperative relationships has led to a denser and more interconnected network, accelerating the flow of innovation, knowledge exchange, and technology transfer.\u003c/p\u003e \u003cp\u003eDespite this growth, network density and average degree remain relatively low, signifying that direct interactions among entities are still limited. This suggests that while core institutions are strengthening their cooperation, peripheral entities still face challenges in accessing the network. Encouraging broader collaboration, particularly with small and medium-sized enterprises (SMEs) and universities, could enhance cross-sector knowledge transfer and innovation diffusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e6.1.2 Stable Cooperation Relationships of Star Nodes in the Defense Decryption Patent Cooperation Network\u003c/h2\u003e \u003cp\u003eUsing centrality measures, the study identified star nodes as key entities that play a leading role in patent cooperation. These nodes\u0026mdash;primarily military enterprises, research institutions, and defense academies\u0026mdash;demonstrate high degree and betweenness centrality, allowing them to act as knowledge hubs. Their stable cooperative relationships enable them to retain critical expertise and guide the development of defense-related technologies.\u003c/p\u003e \u003cp\u003eThe results highlight that star nodes attract collaborations due to their established resources and influence, reinforcing their central position in the network. However, this preferential attachment effect also suggests that new entrants may struggle to integrate into the network, potentially limiting the diversity of innovation. To address this, policies should encourage star nodes to mentor and collaborate with emerging innovators, ensuring that knowledge diffusion is not restricted to a select few institutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e6.1.3 General Interconnection Among Knowledge Elements in Defense Decryption Patent Innovation\u003c/h2\u003e \u003cp\u003eBy analyzing the knowledge network structure, the study found that the network diameter and average path length have gradually decreased, meaning that knowledge elements are becoming more interconnected. This increased interconnection indicates greater opportunities for recombining knowledge elements across different fields, fostering the emergence of dual-use innovations applicable to both military and civilian sectors.\u003c/p\u003e \u003cp\u003eThe findings suggest that defense decryption patents remain concentrated in high-tech fields such as materials science, electronics, and precision manufacturing, while traditional industries, including textiles and construction, show lower integration. Expanding the applicability of defense patents to civilian industries could generate broader societal and economic benefits. Establishing cross-industry partnerships can promote the diffusion of high-value defense innovations into non-military domains, such as healthcare, energy, and infrastructure.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Implications\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e6.2.1 Strengthening the Defense Decryption Patent Cooperation Ecosystem\u003c/h2\u003e \u003cp\u003eTo fully leverage the potential of defense decryption patents, it is essential to establish a robust cooperation ecosystem that facilitates collaboration between military, private enterprises, universities, and research institutions. This collaboration should aim to break down traditional barriers between defense and civilian sectors, allowing for seamless knowledge exchange and technology transfer. By fostering an interconnected network of innovation-driven entities, the commercialization of defense technologies can be significantly accelerated.\u003c/p\u003e \u003cp\u003eOne of the primary strategies to achieve this is by enhancing cross-sector collaboration through dedicated government-backed initiatives that connect defense research institutes with civilian industries. Special programs can be designed to encourage joint research projects, co-development programs, and innovation partnerships, ensuring that military-originated technologies find relevant applications in civilian markets.\u003c/p\u003e \u003cp\u003eMoreover, the government should provide strong financial incentives to facilitate technology transfer. This can include tax reductions, direct subsidies, R\u0026amp;D grants, and patent commercialization funds to encourage firms to participate in defense-related innovation. Many defense technologies have high development and adaptation costs, making it difficult for private enterprises\u0026mdash;especially SMEs and startups\u0026mdash;to engage in technology transfer without financial support. Creating dedicated defense patent transformation funds could help mitigate these barriers and promote innovation.\u003c/p\u003e \u003cp\u003eAnother critical step is improving patent accessibility by establishing open-access national defense patent databases. Such databases should be designed to provide real-time information on declassified defense patents, allowing civilian companies to identify and explore potential applications. A well-organized defense patent repository, along with clear technology licensing guidelines, would streamline commercialization and attract a wider range of industry players.\u003c/p\u003e \u003cp\u003eFinally, to ensure efficient commercialization, governments should develop a streamlined regulatory framework that reduces bureaucratic obstacles associated with technology licensing, patent approvals, and military-civilian technology transfers. Simplified procedures would allow companies to quickly access and integrate military innovations into commercial applications, thus shortening the time from patent declassification to market adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e6.2.2 Supporting Star Institutions as Key Innovation Hubs\u003c/h2\u003e \u003cp\u003eGiven the central role of star institutions in driving innovation, targeted policies and funding should be directed toward supporting these key entities. Star institutions\u0026mdash;including leading defense research centers, military universities, and defense-oriented enterprises\u0026mdash;serve as hubs that connect various entities within the innovation network. Their strategic positioning, extensive R\u0026amp;D capabilities, and access to critical resources make them well-suited to lead collaborative efforts in defense technology commercialization.\u003c/p\u003e \u003cp\u003eOne effective strategy is to encourage the formation of technology clusters, where star institutions, universities, private firms, and defense research centers co-locate in dedicated innovation zones. These clusters should be supported by government grants, preferential policies, and shared infrastructure, ensuring that all participants can benefit from proximity and interdisciplinary collaboration. Successful examples of defense innovation hubs worldwide\u0026mdash;such as the U.S. Defense Innovation Unit (DIU) and Germany\u0026rsquo;s Fraunhofer Institutes\u0026mdash;demonstrate that multi-entity clusters significantly accelerate defense technology transitions.\u003c/p\u003e \u003cp\u003eTo further enhance collaboration, co-development programs should be created to actively involve SMEs and startups in the defense innovation ecosystem. Many of these smaller entities possess specialized technological capabilities but lack direct access to military-related projects. By establishing mentorship programs, joint R\u0026amp;D initiatives, and technology incubators, star institutions can facilitate knowledge sharing and help commercialize emerging technologies.\u003c/p\u003e \u003cp\u003eIn addition to fostering short-term collaborations, governments should establish long-term R\u0026amp;D funding schemes aimed at sustaining innovation ecosystems. These funding schemes should not only support major defense institutions but also allocate resources for high-potential smaller players, ensuring that a diverse set of contributors drive national defense innovation forward.\u003c/p\u003e \u003cp\u003eMoreover, policymakers should introduce regulatory flexibility that allows star institutions to engage in commercial partnerships more effectively. Currently, many defense institutions face regulatory restrictions when collaborating with civilian enterprises. Reforming these policies and streamlining approval processes for military-civilian technology partnerships can enable greater cross-sector integration, leading to faster and more impactful defense technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e6.2.3 Promoting the Marketization of Defense Decryption Patents\u003c/h2\u003e \u003cp\u003eTo ensure that defense-originated innovations reach the commercial market, the government must establish a systematic approach for promoting the marketization of decryption patents. Currently, many high-potential military technologies remain underutilized due to a lack of industry awareness, complex licensing procedures, and insufficient commercialization channels. Addressing these challenges requires proactive government intervention, industry participation, and strategic investment.\u003c/p\u003e \u003cp\u003eA crucial first step is to develop technology matchmaking platforms where defense patent holders can connect with private-sector enterprises seeking innovative solutions. These platforms should provide detailed technology descriptions, potential application areas, licensing terms, and market feasibility assessments to help companies identify relevant patents. Additionally, regular industry-defense innovation forums, technology expos, and matchmaking events can further strengthen direct engagement between military researchers and private-sector investors.\u003c/p\u003e \u003cp\u003eAnother essential measure is facilitating public-private partnerships (PPPs) that leverage government resources and private-sector expertise to scale military innovations for broader use. In particular, venture capital funds and startup accelerators focused on dual-use technologies should be established to help innovative firms commercialize defense patents. By providing targeted financial incentives and risk-sharing mechanisms, these partnerships can reduce commercialization costs and encourage broader market participation.\u003c/p\u003e \u003cp\u003eGovernments should also study international best practices and adopt proven commercialization strategies from leading defense innovation ecosystems. Countries such as the United States, Israel, and South Korea have successfully transitioned military technologies into thriving civilian industries. By analyzing how these nations have structured their defense-to-civilian commercialization pipelines, policymakers can develop China-specific strategies that align with the country\u0026rsquo;s industrial policies and innovation ecosystem.\u003c/p\u003e \u003cp\u003eFinally, defense decryption patents should be integrated into national economic planning, ensuring that defense-driven innovations contribute to long-term economic growth. Strategic sectors such as aerospace, cybersecurity, medical technology, and advanced manufacturing can greatly benefit from military-derived R\u0026amp;D, creating a high-tech, innovation-driven economy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable. This study analyzed declassified patent records and did not involve human participants, human data, or animals.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Innovation Team Project for Ordinary Universities in Guangdong Province (Humanities and Social Sciences) (Grant No. 2023WCXTD026) and Major Scientific Research Projects in Colleges and Universities in Guangdong (Grant No. 2021ZDJS122)(Grant No. 2022ZDJS142).Guangdong Province Philosophy and Social Science Planning Project (GD23YGL28)(GD24CGL26)Guangdong Provincial Society of Logistics and Supply Chain Project (2023LS017C)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.L. conceived and designed the study, collected and curated the patent data, performed the network construction and statistical analyses, interpreted the results, and wrote the main manuscript text. H.L. prepared all figures and tables. H.L. reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are included in this published article and its Supplementary Information files. The record-level patent dataset compiled for this study (including patent identifiers and cleaned bibliographic metadata) are provided as Supplementary Data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasi, A., Hossain, L. \u0026amp; Leydesdorff, L. Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. \u003cem\u003eJ. 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(2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.2991/ASSEHR.K.210519.074\u003c/span\u003e\u003cspan address=\"10.2991/ASSEHR.K.210519.074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Defense decryption patents, Cooperative network, Knowledge network, Social network analysis, Civil-military integration","lastPublishedDoi":"10.21203/rs.3.rs-8510791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8510791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of civil-military integration, the declassification of defense patents has become a crucial mechanism for enhancing technology transfer and maximizing the economic and social value of military innovations. However, the cooperative network dynamics and knowledge evolution of declassified defense patents remain underexplored. This study aims to investigate the structural evolution of defense decryption patent cooperation networks, identify key innovation entities, and analyze the knowledge flow within the defense patent system.\u003c/p\u003e \u003cp\u003eUsing data from the Wisdom Bud database, this research employs social network analysis to examine the cooperative relationships among defense patent innovation entities from 2000 to 2017. Key network indicators such as degree centrality, weighted degree, and network density are analyzed to understand the dynamic evolution of defense decryption patent cooperation. Furthermore, the study constructs a knowledge network to explore the interconnections and combinational potential of different knowledge elements in defense decryption patents.\u003c/p\u003e \u003cp\u003eThe results reveal that the cooperative network of defense decryption patents has undergone a dynamic evolution, transitioning from isolated collaborations to a more interconnected and diversified cooperation model. Star nodes, such as leading defense research institutes and universities, play a central role in maintaining stable cooperation relationships and driving innovation. Additionally, the knowledge network analysis shows that knowledge elements in defense decryption patents are increasingly interlinked, demonstrating high potential for technological recombination and innovation.\u003c/p\u003e \u003cp\u003eThis research contributes to the understanding of defense technology transfer by integrating the analysis of individual networks, cooperative networks, and knowledge network evolution. The findings provide valuable insights for policymakers and enterprises to optimize defense patent collaboration, enhance knowledge flow, and promote civil-military integration. Future research could further explore international defense decryption patents and the strategic value of their patent texts in a global context.\u003c/p\u003e","manuscriptTitle":"Research on the Dynamic Evolution of the Cooperative Network of National Defense Decryption Patents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 05:59:07","doi":"10.21203/rs.3.rs-8510791/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b62a138-1a43-4e6a-acc3-320820559a5b","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62120114,"name":"Humanities/Complex networks"},{"id":62120115,"name":"Social science/Complex networks"},{"id":62120116,"name":"Physical sciences/Mathematics and computing"},{"id":62120117,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-02-23T12:11:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 05:59:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8510791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8510791","identity":"rs-8510791","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}