Deep learning model for community detection fusing network structure and node attributes

Community detection is a research hotspot in network science. Most of the existing discovery methods use edges to represent the similarity of attributes between nodes to implement community exploration. However, empirical studies have shown that there are many other factors besides the similarity of node attributes (e.g., heterophily). In this work, a community discovery model (VNSA) is proposed based on variational graph autoencoders that can fuse network structure and node attributes. Experimental results based on real networks show that this model can effectively complete community detection tasks, and its performance is significantly improved compared with traditional methods (such as Louvain) and deep learning methods (such as Deepwalk). The Model not only better reflects the idea of “homogeneity attracts” in community division but also has certain reference value for relevant practical applications such as friendship recommendations.