2025/01/21 by Chu, Shuangshuang, Xu, Yingnan
#FOS: Computer and information sciences #Social and Information Networks (cs.SI)
paper · doi:10.48550/arxiv.2501.12208
Community discovery is one of the key issues in the study of dynamic social networks. Traditional community discovery algorithms mainly focus on the formation and dissolution of links between nodes, and thus fail to capture richer spatial and temporal patterns underlying network evolution. To address this limitation, we propose GTENN, a spatiotemporal graph neural framework for community discovery in dynamic social networks. GTENN integrates spatial structure and temporal dynamics within a unified embedding architecture. First, Graph Convolutional Networks (GCN) are employed to aggregate latent spatial information and learn expressive node representations at each snapshot. Next, Gated Recurrent Units (GRU) are used to model temporal evolutions of node embeddings, effectively capturing node dynamism and relationship propagation across time. Finally, a Self-Organizing Map (SOM) is applied to the learned spatiotemporal embeddings to cluster nodes and infer their community affiliations. We conduct experiments on four types of dynamic networks, and the results show that GTENN consistently outperforms traditional community discovery algorithms in terms of purity, normalized mutual information, homogeneity, and completeness. These findings demonstrate the superior ability of GTENN to accurately uncover evolving community structures hidden in dynamic social networks.