Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing

Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 October 2025 V1 Latest version Share on Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing Authors : Dongsheng Jing , yu yang 0009-0000-7320-4053 [email protected] , Zhimin Gu , Renjun Feng , Yan Li , and Haitao Jiang Authors Info & Affiliations https://doi.org/10.22541/au.176183623.35993267/v1 Published Electronics Version of record Peer review timeline 153 views 116 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract With the rapid development of information technology, the complexity of data networks is increasing, especially in the electric power system, where data security and privacy protection are of great importance. When electricity data circulates throughout the entire supply chain, it is necessary to track the propagation path of data flow to achieve data protection and traceability. This study aims to address the challenges posed by the complexity of data networks and the security of the electric power system by proposing a data network propagation path tracking model based on Graph Convolutional Networks (GCNs) and BERT. Firstly, a propagation tree is constructed based on the propagation structure. Then, multiple attribute features of data nodes are extracted, and multiple combinations of attribute features are selected as the features of nodes in the propagation path graph. Subsequently, multiple Graph Convolutional Networks are utilized to learn the feature representations of propagation path graphs under different combinations of attribute features. Meanwhile, the BERT model is employed to learn the feature representations of the original text content. Finally, the features learned by the BERT model are integrated with the features learned by the Graph Convolutional Networks for path tracking in data network propagation tasks. Experimental validation was conducted using electricity dataset, demonstrating that the proposed data network propagation path tracking model achieves higher accuracy. Through the application of this model, the electric power system can more effectively address potential data security threats, ensuring the secure transmission and privacy protection of data. Supplementary Material File (multi-feature fusion in graph convolutional networks for data network propagation path tracing.docx) Download 557.22 KB Information & Authors Information Version history V1 Version 1 30 October 2025 Peer review timeline Published Electronics Version of Record 28 Aug 2024 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords information security network security Authors Affiliations Dongsheng Jing State Grid Suzhou Power Supply Co View all articles by this author yu yang 0009-0000-7320-4053 [email protected] State Grid Suzhou Power Supply Co View all articles by this author Zhimin Gu State Grid Jiangsu Electric Power Co Ltd Research Institute View all articles by this author Renjun Feng State Grid Suzhou Power Supply Co View all articles by this author Yan Li State Grid Jiangsu Electric Power Co Ltd Research Institute View all articles by this author Haitao Jiang State Grid Jiangsu Electric Power Co Ltd Research Institute View all articles by this author Metrics & Citations Metrics Article Usage 153 views 116 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dongsheng Jing, yu yang, Zhimin Gu, et al. Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing. Authorea . 30 October 2025. 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