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Open Access

Capturing Global Structural Features and Global Temporal Dependencies in Dynamic Social Networks Using Graph Convolutional Networks for Enhanced Analysis

Fujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
School of Business, Xianda College of Economics and Humanities Shanghai International Studies University, Shanghai 202162, China
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Abstract

Modeling and analysis of complex social networks is an important topic in social computing. Graph convolutional networks (GCNs) are widely used for learning social network embeddings and social network analysis. However, real-world complex social networks, such as Facebook and Math, exhibit significant global structural and dynamic characteristics that are not adequately captured by conventional GCN models. To address the above issues, this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies (GSTGCN). Specifically, we innovatively design a graph coarsening strategy based on the importance of social membership to construct a dynamic diffusion process of graphs. This dynamic diffusion process can be viewed as using higher-order subgraph embeddings to guide the generation of lower-order subgraph embeddings, and we model this process using gate recurrent unit (GRU) to extract comprehensive global structural features of the graph and the evolutionary processes embedded among subgraphs. Furthermore, we design a new evolutionary strategy that incorporates a temporal self-attention mechanism to enhance the extraction of global temporal dependencies of dynamic networks by GRU. GSTGCN outperforms current state-of-the-art network embedding methods in important social networks tasks such as link prediction and financial fraud identification.

Keywords

dynamic social networkgraph convolutional networknetwork representation learninglink prediction

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Journal of Social Computing
Volume 6 Issue 2,
June 2025
Pages 126-144
DOI: 10.23919/JSC.2025.0008
Cite this article:
Wu L, Li B, Guo K, et al. Capturing Global Structural Features and Global Temporal Dependencies in Dynamic Social Networks Using Graph Convolutional Networks for Enhanced Analysis. Journal of Social Computing, 2025, 6(2): 126-144. https://doi.org/10.23919/JSC.2025.0008

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Received: 17 December 2024
Revised: 29 April 2025
Accepted: 18 May 2025
Published: 30 June 2025
© The author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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