CDBMA: Community Detection in Heterogeneous Networks Based on Multi-attention Mechanism

Community detection in complex networks is a fundamental task in network analysis. With the continuous evolution of social networks, network structures are becoming more complex and often contain rich heterogeneous information. Traditional community detection methods can only utilize shallow topological features and fail to leverage the rich heterogeneous information in these networks, making community detection in heterogeneous networks a new challenge. In this paper, we propose a community detection model for heterogeneous networks based on multi-attention mechanisms. Our model consists of a structural information encoder and a semantic information encoder. The structural information encoder proposes a subgraph sampler to extract subgraphs around target nodes, uses type attention to aggregate the influence of different types of nodes, and learns the heterogeneous structural information of the network. The semantic information encoder uses node attention to learn the importance of high-order neighbor nodes based on meta-paths, uses semantic attention to learn the weights of different meta-paths, and fuses the content semantic information on different meta-paths to learn the content semantic information of the heterogeneous network. Joint optimization of structural and semantic encoders is achieved through self-supervised learning, addressing the dependence on community labels. We evaluate our model on four real-world datasets, and the results show that our algorithm outperforms several community detection state-of-the-art methods, especially approximate 10% improvement in the NMI metric on the ACM and Freebase datasets, and approximate 20% improvement in the ARI metric on the ACM dataset.