Abstract: Community detection in social networks is important for applications such as information diffusion, recommendation, and advertising. However, existing methods still face challenges in feature fusion, sparse graph modeling, and multi-source information utilization. To address these issues, this paper proposed a structure-enhanced deep clustering (SDC) model for network community detection, which consisted of four key modules. First, the topology enhancement module built an enhanced adjacency matrix by modeling second-order similarity between nodes, which alleviated the problem of missing high-order relations in sparse social networks. Second, the multi-view feature fusion module dynamically fused node attributes and topology features at the node level, and integrated semantic information from both the original and enhanced graphs at the graph level. Third, the multi-source distribution fusion clustering module used learnable weights to integrate clustering information from different feature spaces at the distribution level, balancing local topology and global semantics. Finally, the dual self-supervised module optimized the model through Kullback-Leibler (KL) divergence alignment, node reconstruction, and similarity constraints. Experiments show that compared with mainstream baseline methods, SDC model improves ACC, NMI, ARI, and F1 by an average of 3.80%, 9.09%, 11.21%, and 7.43%, respectively on the three benchmark datasets. Simulations based on Facebook interaction data also demonstrate the ability of SDC model to capture community structure evolution.

Key words: social network, community detection, structure-enhanced, deep clustering, attention mechanism