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

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.