IgG4-Related Disease: Leading International Co-Authorship Networks and Future Research Directions

IgG4-Related Disease: Leading International Co-Authorship Networks and Future Research Directions | 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 Research Article IgG4-Related Disease: Leading International Co-Authorship Networks and Future Research Directions Naruaki Ogasawara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5001977/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 Aim This study aims to analyze the structure of co-author networks in IgG4-related disease (IgG4-RD) research from 2000 to 2023, using data from the Web of Science (WoS) Core Collection. The goal is to identify collaborative relationships, key researchers, and trends within the research community over time. Method I conducted a comprehensive network analysis of 5,310 articles on IgG4-RD published between 2000 and 2023, as indexed in the WoS Core Collection. The analysis was performed using Python (Version 3.10.5) within the PyCharm integrated development environment (IDE) (Software Version 2022.1.3). Macro-level indicators, including network density, clustering coefficient, number of components, and average path length, were used to assess the overall network structure. Micro-level indicators, such as degree centrality, closeness centrality, and betweenness centrality, were employed to evaluate the influence and connectivity of individual researchers within the network. Result The co-authorship network analysis revealed a fragmented structure with isolated clusters of researchers throughout the studied periods: 2000–2009, 2010–2019, and 2020–2023. Network density remained low, reflecting limited direct collaborations among researchers, while high clustering coefficients indicated the formation of tight-knit collaborative groups. The number of components decreased slightly over time, suggesting a gradual improvement in connectivity. Key researchers, including John H. Stone, Mitsuhiro Kawano, and Kazuichi Okazaki, consistently exhibited high centrality metrics, highlighting their pivotal roles in bridging research clusters and fostering collaboration in IgG4-RD. Conclusion The analysis of IgG4-RD research co-authorship networks from 2000 to 2023 reveals a field characterized by strong localized collaboration but overall low network cohesion. While key researchers have played significant roles in connecting various clusters, the network's fragmented nature suggests opportunities for enhancing broader collaborative efforts. Improving international and interdisciplinary connections could foster more comprehensive research and accelerate advancements in the understanding and treatment of IgG4-RD. Medical Informatics Immunology Allergy & Immune Disorders Bioinformatics Information Retrieval and Management IgG4-related disease co-authorship network network analysis research collaboration research trends key researchers research strategies internal medicine planning future collaborative studies Figures Figure 1 Figure 2 Figure 3 Introduction IgG4-related disease (IgG4-RD) has rapidly gained attention since 2000, becoming an important research area in Internal Medicine 1 . This disease is known to cause chronic inflammation in various organs and possesses distinctive pathological features 2 – 3 . While IgG4-RD presents a variety of clinical symptoms requiring accurate diagnosis and treatment 4 , the etiology and pathogenesis remain largely enigmatic 5 . Research on IgG4-RD has been conducted actively, primarily in Japan and the United States 6 , achieving results through diverse approaches. Since IgG4-RD was established as a new disease in the 2000s, many researchers have been working to elucidate its mechanisms. Researchers often collaborate on writing papers, known as co-authorship. By investigating co-authors in specific fields, the research connections between scientists can be identified. Analyzing these connections through nodes and links can provide insights into research collaboration and influence within a specific research area. This paper conducts a network analysis of co-authors on IgG4-RD papers listed from 2000 to 2023 in Web of Science (WoS) Core Collection, which includes about 21,000 of the world's most influential academic journals. The main objectives of the analysis are to analyze and visualize the collaborative relationships in IgG4-RD research, clarify the changes in collaboration, and investigate the influence of authors. Material and methods The present study investigates the co-authorship patterns in IgG4-RD research papers. I utilized the WoS Core Collection database, conducting a "Topic search" with the keyword "IgG4-related disease" to analyze a total of 5,310 articles published between 2000 and 2023 (as of August 2024). In this analysis, I examined who collaborated with whom in co-authoring these papers. I conducted network analysis using the Python programming language (version 3.10.5) within the integrated development environment (IDE) PyCharm (software version 2022.1.3). This study employed methodology-established principles of social network analysis 7 . I carried out the analysis in two main parts: Macro-level Metrics: Network Density Calculated as the ratio of the number of edges to the maximum possible edges between all nodes. Clustering Coefficient Measured the extent to which nodes form clusters by considering the number of edges among neighboring nodes and calculating the average. Components Identified and counted the number of subgraphs (components) where nodes are mutually connected. Average Path Length Evaluated the average "distance" between nodes by calculating the overall average path length in the network 8 . Micro-level Metrics: Degree Centrality Measured the importance of each node by counting the number of edges it has in the network. Closeness Centrality Defined as the inverse of the sum of the shortest path lengths from a node to all other nodes, measuring how close each node is to others in the network. Betweenness Centrality Assessed the extent to which a node lies on the shortest paths between other nodes, indicating its importance in information transmission within the network 8 – 9 . The significance of these macro-level metrics in understanding the structure of scientific collaboration networks and these micro-level centrality measures in scientific collaboration networks has been well documented and used 8 – 9 . Through these analyses, I can identify collaborative relationships and influential researchers in IgG4-RD research. This information may be useful for understanding research trends and planning future collaborative studies. Results The co-authorship network analysis of IgG4-RD research from 2000 to 2023 revealed insights into the collaborative structures among researchers across three distinct periods: 2000–2009, 2010–2019, and 2020–2023. In this network, each node represents an author, and the links between nodes signify co-authorship. This analysis employed macro-level and micro-level metrics to evaluate the characteristics and dynamics of the network over time. 2000–2009 Network Analysis The co-authorship network during 2000–2009 exhibited a low density of 0.0137, indicating that only 1.37% of the potential connections between authors were realized. The average clustering coefficient was notably high at 0.9343, suggesting that authors were likely to form clusters, often collaborating within tight-knit groups. The network comprised 72 components, highlighting a fragmented structure with many isolated clusters of collaboration. Due to the network's sparse nature, the average distance between nodes was infinite, indicating that not all authors were reachable from others within the network (Table 1, Fig. 1 ) 10 . Regarding micro-level indicators, Yoh Zen demonstrated the highest degree centrality (0.0837) and closeness centrality (0.0837) and was among the top in betweenness centrality (0.0040), reflecting his pivotal role in connecting various research clusters during this period (Table 2–4). 2010–2019 Network Analysis During the 2010–2019 period, the network density further decreased to 0.0010, reflecting an even more dispersed network with limited direct collaborations among authors. The average clustering coefficient remained high at 0.9188, indicating a persistent tendency for authors to form clusters, though these clusters were part of a significantly fragmented network comprising 1,248 components. Similar to the earlier period, the average distance remained infinite, suggesting a lack of connectivity across the entire network (Table 1, Fig. 2 ) 10 . Key researchers with the highest degree centrality included John H. Stone (0.0376), Mitsuhiro Kawano (0.0306), and Kazuichi Okazaki (0.0255), who were also prominent in closeness centrality, indicating their central roles within their respective clusters. Notably, Kim M. H. exhibited the highest betweenness centrality (0.036), highlighting his crucial position in bridging different parts of the network (Table 2–4). 2020–2023 Network Analysis In the most recent period of 2020–2023, the network density slightly increased to 0.0012, yet the network remained sparsely connected overall. The average clustering coefficient was 0.9368, continuing the trend of highly clustered but isolated groups of collaboration. The number of components decreased to 1,074, showing a slight improvement in network connectivity compared to the previous period. However, the average distance remained infinite, consistent with the ongoing fragmentation of the network (Table 1, Fig. 3 ) 10 . John H. Stone remained a prominent figure with the highest degree centrality (0.0317) and was closely followed by other influential researchers such as Kazuichi Okazaki (0.0281) and Mitsuhiro Kawano (0.0267). In terms of betweenness centrality, John H. Stone (0.0093) continued to occupy critical positions, facilitating connections across disparate research clusters (Table 2–4). Summary Overall, the network analysis across all three periods demonstrates a consistently fragmented structure with isolated clusters of researchers. The high clustering coefficients indicate strong local collaboration, but the overall low network density and large number of components suggest limited broader connectivity among researchers in IgG4-RD. The analysis highlights key researchers who have played central roles in fostering collaboration and connecting different research clusters, underscoring their influence in the field over the past two decades. Discussion The present study analyzed the co-authorship network of IgG4-RD research from 2000 to 2023, utilizing data from the WoS Core Collection. By applying both macro-level and micro-level network metrics, I sought to understand the collaborative landscape among researchers in this field. Our analysis revealed several key characteristics of the co-authorship network, including its overall fragmentation, the prominence of specific researchers, and the trends in collaboration over time. The co-authorship network for the entire period was characterized by a relatively low network density across all three sub-periods analyzed (2000–2009, 2010–2019, and 2020–2023). For instance, the density remained below 0.014, indicating that only a small fraction of the possible collaborative connections between authors were realized. This finding suggested that although IgG4-RD was a growing research area, the overall collaboration among researchers was limited, with many potential connections not being actualized. However, the high clustering coefficient across all periods (ranging from 0.9188 to 0.9368) indicated that when collaborations did occur, they often formed tightly knit groups or clusters. This pattern was typical in specialized fields where researchers frequently collaborated within familiar circles, potentially hindering broader interdisciplinary or international collaborations. The analysis of components revealed a highly fragmented network with a large number of disconnected subgroups. For example, the network during 2010–2019 consisted of 1,248 components, while the number slightly reduced to 1,074 in the 2020–2023 period. The persistence of such a high number of components suggested that many researchers or groups were working in isolation, possibly due to niche specializations or regional collaborations that did not extend beyond their immediate networks. This fragmentation could limit the potential for cross-pollination of ideas and collaborative breakthroughs, which are often facilitated by a more interconnected research network. Regarding micro-level metrics, certain researchers emerged as central figures within the co-authorship network. John H. Stone, Mitsuhiro Kawano, and Kazuichi Okazaki consistently displayed a high centrality degree, signifying their active involvement in multiple collaborations. Notably, John H. Stone also held the third betweenness centrality in the 2020–2023 period (0.093), indicating his role as a crucial intermediary who connected disparate clusters within the network. These central researchers not only contributed to the volume of research output but also played pivotal roles in bridging different research groups, thereby enhancing the flow of information and collaboration within the field. The average path length remained infinite throughout all periods, reflecting the disconnected nature of the network. This was in stark contrast to the "small world" phenomenon, where networks typically exhibit short paths between any two nodes, facilitating efficient information dissemination. The lack of a small-world structure in IgG4-RD research indicated that knowledge and research findings might not be easily accessible across the entire field, potentially slowing the overall progress in understanding and treating this complex disease. The observed network structure had significant implications for the future of IgG4-RD research. The low density and high fragmentation suggested opportunities for initiatives aimed at fostering broader collaborations, particularly those that bridged gaps between isolated research clusters. Encouraging international collaborations and interdisciplinary research might help to integrate the network further, reducing the number of isolated components and enhancing the overall connectivity. Such efforts could lead to more comprehensive studies and accelerate advancements in understanding IgG4-RD. In conclusion, the co-authorship network analysis of IgG4-RD research from 2000 to 2023 highlighted a field that was rich in localized collaboration but fragmented on a broader scale. Key researchers played crucial roles in connecting various clusters, yet the overall network remained disjointed. Addressing these structural challenges through targeted collaborative efforts could enhance the efficiency and impact of future research, ultimately benefiting the clinical management and understanding of IgG4-RD. Conclusion The co-authorship network analysis of IgG4-RD research from 2000 to 2023 reveals the evolving landscape of collaboration among researchers in this specialized field. This period was chosen as it marks the rapid advancement of IgG4-RD research, beginning with studies focused on the pancreas in the early 2000s. In 2001, autoimmune pancreatitis was reported, and subsequently, symptoms were recognized in other organs, leading to the recognition of IgG4-RD as a systemic disease. This established the concept of IgG4-RD. Despite the increasing trend toward cooperation, the network demonstrates low overall cohesion, indicating that many potential collaborations remain unrealized. The network density was relatively low, suggesting that only a small fraction of potential collaborations between authors were actualized. This indicates a fragmented collaborative structure, with numerous isolated subgroups within the network. However, the clustering coefficient suggests that when collaborations do occur, they often form tight-knit clusters of researchers. This reflects a tendency for researchers to work closely within specific groups rather than across the broader network. The average path length remained relatively high, suggesting that many researchers are not directly connected and that information dissemination across the network may be hindered by the absence of direct or short paths between researchers. At the micro level, researchers with a high degree centrality, indicating numerous direct collaborations, often served as pivotal figures within their respective clusters. Closeness centrality highlights researchers who are positioned near the center of the network, thereby having more direct access to others. Betweenness centrality emphasizes the importance of certain individuals who act as bridges between different clusters, facilitating connections that might not otherwise exist. Although network density was low, the study identified a core group of influential researchers who play significant roles in bridging gaps within the network. These central figures not only drive collaboration within their clusters but also act as vital connectors between disparate groups, enhancing the overall flow of information and knowledge across the field. The findings underscore the importance of fostering greater interconnectivity and collaboration across the network to overcome the current fragmentation and enhance overall research productivity in the field of IgG4-RD. During the period from 2010 to 2019, international collaboration was hypothesized to have advanced significantly, particularly following the publication of the "Comprehensive Diagnostic Criteria for IgG4-RD" in Japan in 2011 and the "2019 ACR/EULAR Classification Criteria" by the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR). However, the actual network analysis revealed that while there were key international collaborations, the overall network cohesion remained low. Future efforts should focus on strategies to increase network density, such as promoting cross-group collaborations and expanding the involvement of emerging researchers. These initiatives could foster a more integrated and dynamic research community in the field of IgG4-RD. Further studies are needed to elucidate the mechanisms of IgG4-RD and to develop more sophisticated therapeutic approaches. Although diagnostic and classification criteria exist, there seems to be a need to unify and establish international diagnostic criteria without regional or racial differences in content. Moreover, since IgG4-RD is a disease that affects multiple organs, a multidisciplinary research approach with the cooperation of specialists and researchers from various fields, not only internal medicine physicians, will be important. Abbreviations IgG4-RD, IgG4-related disease; WoS, Web of Science; IDE, Integrated Development Environment. Declarations Funding statement: none Conflict of interest disclosure statement: none Ethics approval statement: not applicable for this article. References Wallace ZS, Katz G, Hernandez-Barco YG, Baker MC (2024) Current and future advances in practice: IgG4-related disease. Rheumatol Adv Pract. ;8(2) Czarnywojtek A, Agaimy A, Pietrończyk K et al (2024) IgG4-related disease: an update on pathology and diagnostic criteria with a focus on salivary gland manifestations. Virchows Arch 484:381–399 Pinheiro FAG, Pereira IA, de Souza AWS et al (2024) IgG4-related disease—rare but you should not forget it. Adv Rheumatol 64:35 Wu S, Wang H (2023) IgG4-related digestive diseases: diagnosis and treatment. Front Immunol. Oct Deshpande V et al (2012) Consensus statement on the pathology of IgG4-related disease. Mod Pathol 25(9):1181–1192 Lv Z, Wu L, Lu Y, Liu S, Li Q (2023) Bibliometric analysis of IgG4-related disease research from 2003 to 2022 based on Web of Science Core Collection Databases. Clin Rheumatol 42(1):15–27 Wasserman S, Faust K (1994) Social network analysis: Methods and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511815478 Newman M (2001) Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys Rev E 64(1):016132 Newman ME (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 98(2):404–409 Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table1.xlsx Network Metrics Table2.xlsx Top 20 Nodes by Degree Centrality Table3.xlsx Top 20 Nodes by Closeness Centrality Table4.xlsx Top 20 Nodes by Betweenness Centrality 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. 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Directions\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIgG4-related disease (IgG4-RD) has rapidly gained attention since 2000, becoming an important research area in Internal Medicine \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This disease is known to cause chronic inflammation in various organs and possesses distinctive pathological features \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. While IgG4-RD presents a variety of clinical symptoms requiring accurate diagnosis and treatment \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, the etiology and pathogenesis remain largely enigmatic \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch on IgG4-RD has been conducted actively, primarily in Japan and the United States \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, achieving results through diverse approaches. Since IgG4-RD was established as a new disease in the 2000s, many researchers have been working to elucidate its mechanisms. Researchers often collaborate on writing papers, known as co-authorship. By investigating co-authors in specific fields, the research connections between scientists can be identified. Analyzing these connections through nodes and links can provide insights into research collaboration and influence within a specific research area.\u003c/p\u003e \u003cp\u003eThis paper conducts a network analysis of co-authors on IgG4-RD papers listed from 2000 to 2023 in Web of Science (WoS) Core Collection, which includes about 21,000 of the world's most influential academic journals. The main objectives of the analysis are to analyze and visualize the collaborative relationships in IgG4-RD research, clarify the changes in collaboration, and investigate the influence of authors.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThe present study investigates the co-authorship patterns in IgG4-RD research papers. I utilized the WoS Core Collection database, conducting a \"Topic search\" with the keyword \"IgG4-related disease\" to analyze a total of 5,310 articles published between 2000 and 2023 (as of August 2024). In this analysis, I examined who collaborated with whom in co-authoring these papers. I conducted network analysis using the Python programming language (version 3.10.5) within the integrated development environment (IDE) PyCharm (software version 2022.1.3). This study employed methodology-established principles of social network analysis \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. I carried out the analysis in two main parts:\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMacro-level Metrics:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eNetwork Density\u003c/strong\u003e \u003cp\u003eCalculated as the ratio of the number of edges to the maximum possible edges between all nodes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClustering Coefficient\u003c/strong\u003e \u003cp\u003eMeasured the extent to which nodes form clusters by considering the number of edges among neighboring nodes and calculating the average.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComponents\u003c/strong\u003e \u003cp\u003eIdentified and counted the number of subgraphs (components) where nodes are mutually connected.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAverage Path Length\u003c/strong\u003e \u003cp\u003eEvaluated the average \"distance\" between nodes by calculating the overall average path length in the network \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMicro-level Metrics:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDegree Centrality\u003c/strong\u003e \u003cp\u003eMeasured the importance of each node by counting the number of edges it has in the network.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCloseness Centrality\u003c/strong\u003e \u003cp\u003eDefined as the inverse of the sum of the shortest path lengths from a node to all other nodes, measuring how close each node is to others in the network.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBetweenness Centrality\u003c/strong\u003e \u003cp\u003eAssessed the extent to which a node lies on the shortest paths between other nodes, indicating its importance in information transmission within the network \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe significance of these macro-level metrics in understanding the structure of scientific collaboration networks and these micro-level centrality measures in scientific collaboration networks has been well documented and used \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Through these analyses, I can identify collaborative relationships and influential researchers in IgG4-RD research. This information may be useful for understanding research trends and planning future collaborative studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe co-authorship network analysis of IgG4-RD research from 2000 to 2023 revealed insights into the collaborative structures among researchers across three distinct periods: 2000\u0026ndash;2009, 2010\u0026ndash;2019, and 2020\u0026ndash;2023. In this network, each node represents an author, and the links between nodes signify co-authorship. This analysis employed macro-level and micro-level metrics to evaluate the characteristics and dynamics of the network over time.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2000\u0026ndash;2009 Network Analysis\u003c/h2\u003e \u003cp\u003eThe co-authorship network during 2000\u0026ndash;2009 exhibited a low density of 0.0137, indicating that only 1.37% of the potential connections between authors were realized. The average clustering coefficient was notably high at 0.9343, suggesting that authors were likely to form clusters, often collaborating within tight-knit groups. The network comprised 72 components, highlighting a fragmented structure with many isolated clusters of collaboration. Due to the network's sparse nature, the average distance between nodes was infinite, indicating that not all authors were reachable from others within the network (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding micro-level indicators, Yoh Zen demonstrated the highest degree centrality (0.0837) and closeness centrality (0.0837) and was among the top in betweenness centrality (0.0040), reflecting his pivotal role in connecting various research clusters during this period (Table\u0026nbsp;2\u0026ndash;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2010\u0026ndash;2019 Network Analysis\u003c/h2\u003e \u003cp\u003eDuring the 2010\u0026ndash;2019 period, the network density further decreased to 0.0010, reflecting an even more dispersed network with limited direct collaborations among authors. The average clustering coefficient remained high at 0.9188, indicating a persistent tendency for authors to form clusters, though these clusters were part of a significantly fragmented network comprising 1,248 components. Similar to the earlier period, the average distance remained infinite, suggesting a lack of connectivity across the entire network (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKey researchers with the highest degree centrality included John H. Stone (0.0376), Mitsuhiro Kawano (0.0306), and Kazuichi Okazaki (0.0255), who were also prominent in closeness centrality, indicating their central roles within their respective clusters. Notably, Kim M. H. exhibited the highest betweenness centrality (0.036), highlighting his crucial position in bridging different parts of the network (Table\u0026nbsp;2\u0026ndash;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2020\u0026ndash;2023 Network Analysis\u003c/h2\u003e \u003cp\u003eIn the most recent period of 2020\u0026ndash;2023, the network density slightly increased to 0.0012, yet the network remained sparsely connected overall. The average clustering coefficient was 0.9368, continuing the trend of highly clustered but isolated groups of collaboration. The number of components decreased to 1,074, showing a slight improvement in network connectivity compared to the previous period. However, the average distance remained infinite, consistent with the ongoing fragmentation of the network (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eJohn H. Stone remained a prominent figure with the highest degree centrality (0.0317) and was closely followed by other influential researchers such as Kazuichi Okazaki (0.0281) and Mitsuhiro Kawano (0.0267). In terms of betweenness centrality, John H. Stone (0.0093) continued to occupy critical positions, facilitating connections across disparate research clusters (Table\u0026nbsp;2\u0026ndash;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSummary\u003c/h2\u003e \u003cp\u003eOverall, the network analysis across all three periods demonstrates a consistently fragmented structure with isolated clusters of researchers. The high clustering coefficients indicate strong local collaboration, but the overall low network density and large number of components suggest limited broader connectivity among researchers in IgG4-RD. The analysis highlights key researchers who have played central roles in fostering collaboration and connecting different research clusters, underscoring their influence in the field over the past two decades.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study analyzed the co-authorship network of IgG4-RD research from 2000 to 2023, utilizing data from the WoS Core Collection. By applying both macro-level and micro-level network metrics, I sought to understand the collaborative landscape among researchers in this field. Our analysis revealed several key characteristics of the co-authorship network, including its overall fragmentation, the prominence of specific researchers, and the trends in collaboration over time.\u003c/p\u003e \u003cp\u003eThe co-authorship network for the entire period was characterized by a relatively low network density across all three sub-periods analyzed (2000\u0026ndash;2009, 2010\u0026ndash;2019, and 2020\u0026ndash;2023). For instance, the density remained below 0.014, indicating that only a small fraction of the possible collaborative connections between authors were realized. This finding suggested that although IgG4-RD was a growing research area, the overall collaboration among researchers was limited, with many potential connections not being actualized. However, the high clustering coefficient across all periods (ranging from 0.9188 to 0.9368) indicated that when collaborations did occur, they often formed tightly knit groups or clusters. This pattern was typical in specialized fields where researchers frequently collaborated within familiar circles, potentially hindering broader interdisciplinary or international collaborations.\u003c/p\u003e \u003cp\u003eThe analysis of components revealed a highly fragmented network with a large number of disconnected subgroups. For example, the network during 2010\u0026ndash;2019 consisted of 1,248 components, while the number slightly reduced to 1,074 in the 2020\u0026ndash;2023 period. The persistence of such a high number of components suggested that many researchers or groups were working in isolation, possibly due to niche specializations or regional collaborations that did not extend beyond their immediate networks. This fragmentation could limit the potential for cross-pollination of ideas and collaborative breakthroughs, which are often facilitated by a more interconnected research network.\u003c/p\u003e \u003cp\u003eRegarding micro-level metrics, certain researchers emerged as central figures within the co-authorship network. John H. Stone, Mitsuhiro Kawano, and Kazuichi Okazaki consistently displayed a high centrality degree, signifying their active involvement in multiple collaborations. Notably, John H. Stone also held the third betweenness centrality in the 2020\u0026ndash;2023 period (0.093), indicating his role as a crucial intermediary who connected disparate clusters within the network. These central researchers not only contributed to the volume of research output but also played pivotal roles in bridging different research groups, thereby enhancing the flow of information and collaboration within the field.\u003c/p\u003e \u003cp\u003eThe average path length remained infinite throughout all periods, reflecting the disconnected nature of the network. This was in stark contrast to the \"small world\" phenomenon, where networks typically exhibit short paths between any two nodes, facilitating efficient information dissemination. The lack of a small-world structure in IgG4-RD research indicated that knowledge and research findings might not be easily accessible across the entire field, potentially slowing the overall progress in understanding and treating this complex disease.\u003c/p\u003e \u003cp\u003eThe observed network structure had significant implications for the future of IgG4-RD research. The low density and high fragmentation suggested opportunities for initiatives aimed at fostering broader collaborations, particularly those that bridged gaps between isolated research clusters. Encouraging international collaborations and interdisciplinary research might help to integrate the network further, reducing the number of isolated components and enhancing the overall connectivity. Such efforts could lead to more comprehensive studies and accelerate advancements in understanding IgG4-RD.\u003c/p\u003e \u003cp\u003eIn conclusion, the co-authorship network analysis of IgG4-RD research from 2000 to 2023 highlighted a field that was rich in localized collaboration but fragmented on a broader scale. Key researchers played crucial roles in connecting various clusters, yet the overall network remained disjointed. Addressing these structural challenges through targeted collaborative efforts could enhance the efficiency and impact of future research, ultimately benefiting the clinical management and understanding of IgG4-RD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe co-authorship network analysis of IgG4-RD research from 2000 to 2023 reveals the evolving landscape of collaboration among researchers in this specialized field. This period was chosen as it marks the rapid advancement of IgG4-RD research, beginning with studies focused on the pancreas in the early 2000s. In 2001, autoimmune pancreatitis was reported, and subsequently, symptoms were recognized in other organs, leading to the recognition of IgG4-RD as a systemic disease. This established the concept of IgG4-RD.\u003c/p\u003e \u003cp\u003eDespite the increasing trend toward cooperation, the network demonstrates low overall cohesion, indicating that many potential collaborations remain unrealized. The network density was relatively low, suggesting that only a small fraction of potential collaborations between authors were actualized. This indicates a fragmented collaborative structure, with numerous isolated subgroups within the network. However, the clustering coefficient suggests that when collaborations do occur, they often form tight-knit clusters of researchers. This reflects a tendency for researchers to work closely within specific groups rather than across the broader network. The average path length remained relatively high, suggesting that many researchers are not directly connected and that information dissemination across the network may be hindered by the absence of direct or short paths between researchers.\u003c/p\u003e \u003cp\u003eAt the micro level, researchers with a high degree centrality, indicating numerous direct collaborations, often served as pivotal figures within their respective clusters. Closeness centrality highlights researchers who are positioned near the center of the network, thereby having more direct access to others. Betweenness centrality emphasizes the importance of certain individuals who act as bridges between different clusters, facilitating connections that might not otherwise exist. Although network density was low, the study identified a core group of influential researchers who play significant roles in bridging gaps within the network. These central figures not only drive collaboration within their clusters but also act as vital connectors between disparate groups, enhancing the overall flow of information and knowledge across the field.\u003c/p\u003e \u003cp\u003eThe findings underscore the importance of fostering greater interconnectivity and collaboration across the network to overcome the current fragmentation and enhance overall research productivity in the field of IgG4-RD. During the period from 2010 to 2019, international collaboration was hypothesized to have advanced significantly, particularly following the publication of the \"Comprehensive Diagnostic Criteria for IgG4-RD\" in Japan in 2011 and the \"2019 ACR/EULAR Classification Criteria\" by the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR). However, the actual network analysis revealed that while there were key international collaborations, the overall network cohesion remained low.\u003c/p\u003e \u003cp\u003eFuture efforts should focus on strategies to increase network density, such as promoting cross-group collaborations and expanding the involvement of emerging researchers. These initiatives could foster a more integrated and dynamic research community in the field of IgG4-RD. Further studies are needed to elucidate the mechanisms of IgG4-RD and to develop more sophisticated therapeutic approaches. Although diagnostic and classification criteria exist, there seems to be a need to unify and establish international diagnostic criteria without regional or racial differences in content. Moreover, since IgG4-RD is a disease that affects multiple organs, a multidisciplinary research approach with the cooperation of specialists and researchers from various fields, not only internal medicine physicians, will be important.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIgG4-RD, IgG4-related disease; WoS, Web of Science; IDE, Integrated Development Environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure statement:\u003c/strong\u003e none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement:\u003c/strong\u003e not applicable for this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWallace ZS, Katz G, Hernandez-Barco YG, Baker MC (2024) Current and future advances in practice: IgG4-related disease. Rheumatol Adv Pract. ;8(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzarnywojtek A, Agaimy A, Pietrończyk K et al (2024) IgG4-related disease: an update on pathology and diagnostic criteria with a focus on salivary gland manifestations. Virchows Arch 484:381\u0026ndash;399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinheiro FAG, Pereira IA, de Souza AWS et al (2024) IgG4-related disease\u0026mdash;rare but you should not forget it. Adv Rheumatol 64:35\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, Wang H (2023) IgG4-related digestive diseases: diagnosis and treatment. Front Immunol. Oct\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeshpande V et al (2012) Consensus statement on the pathology of IgG4-related disease. Mod Pathol 25(9):1181\u0026ndash;1192\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv Z, Wu L, Lu Y, Liu S, Li Q (2023) Bibliometric analysis of IgG4-related disease research from 2003 to 2022 based on Web of Science Core Collection Databases. Clin Rheumatol 42(1):15\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWasserman S, Faust K (1994) Social network analysis: Methods and applications. Cambridge University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/CBO9780511815478\u003c/span\u003e\u003cspan address=\"10.1017/CBO9780511815478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman M (2001) Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys Rev E 64(1):016132\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman ME (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 98(2):404\u0026ndash;409\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509\u0026ndash;512\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The Japanese Society of Internal Medicine","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":"IgG4-related disease, co-authorship network, network analysis, research collaboration, research trends, key researchers, research strategies, internal medicine, planning future collaborative studies","lastPublishedDoi":"10.21203/rs.3.rs-5001977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5001977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study aims to analyze the structure of co-author networks in IgG4-related disease (IgG4-RD) research from 2000 to 2023, using data from the Web of Science (WoS) Core Collection. The goal is to identify collaborative relationships, key researchers, and trends within the research community over time.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eI conducted a comprehensive network analysis of 5,310 articles on IgG4-RD published between 2000 and 2023, as indexed in the WoS Core Collection. The analysis was performed using Python (Version 3.10.5) within the PyCharm integrated development environment (IDE) (Software Version 2022.1.3). Macro-level indicators, including network density, clustering coefficient, number of components, and average path length, were used to assess the overall network structure. Micro-level indicators, such as degree centrality, closeness centrality, and betweenness centrality, were employed to evaluate the influence and connectivity of individual researchers within the network.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe co-authorship network analysis revealed a fragmented structure with isolated clusters of researchers throughout the studied periods: 2000\u0026ndash;2009, 2010\u0026ndash;2019, and 2020\u0026ndash;2023. Network density remained low, reflecting limited direct collaborations among researchers, while high clustering coefficients indicated the formation of tight-knit collaborative groups. The number of components decreased slightly over time, suggesting a gradual improvement in connectivity. Key researchers, including John H. Stone, Mitsuhiro Kawano, and Kazuichi Okazaki, consistently exhibited high centrality metrics, highlighting their pivotal roles in bridging research clusters and fostering collaboration in IgG4-RD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe analysis of IgG4-RD research co-authorship networks from 2000 to 2023 reveals a field characterized by strong localized collaboration but overall low network cohesion. While key researchers have played significant roles in connecting various clusters, the network's fragmented nature suggests opportunities for enhancing broader collaborative efforts. Improving international and interdisciplinary connections could foster more comprehensive research and accelerate advancements in the understanding and treatment of IgG4-RD.\u003c/p\u003e","manuscriptTitle":"IgG4-Related Disease: Leading International Co-Authorship Networks and Future Research Directions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 04:19:11","doi":"10.21203/rs.3.rs-5001977/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":"3ed88d0e-9989-48b0-8de0-6032abb23401","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36827618,"name":"Medical Informatics"},{"id":36827619,"name":"Immunology"},{"id":36827620,"name":"Allergy \u0026 Immune Disorders"},{"id":36827621,"name":"Bioinformatics"},{"id":36827622,"name":"Information Retrieval and Management"}],"tags":[],"updatedAt":"2024-09-02T04:19:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-02 04:19:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5001977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5001977","identity":"rs-5001977","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}