Graph Diffusion Enhanced Contrastive Representation Learning on Topology and Feature Space for Community Detection

Community detection is a fundamental task for understanding complex networks. Many traditional community detection methods only focus on the relationships between nodes in the topology space (i.e., the topology graph) and node attributes. These methods do not fully consider the relationships between nodes in the feature space (i.e., the feature graph) and the global structure of the graph. To this end, we introduce an innovative approach termed Graph Diffusion enhanced Contrastive representation Learning (GDCL) method on topology and feature space for community detection. Specifically, we first construct a feature graph from node attributes to capture the relationships between nodes in the feature space. Based on the topology graph and feature graph, we deploy the graph diffusion module to generate topology diffusion and feature diffusion graphs respectively. Then, we leverage the shared graph convolutional and graph contrastive learning modules to learn node representations of the local and global structures of the graphs. Simultaneously, we employ the hidden layer adaptive embedding fusion mechanism to obtain high-quality node representations. Finally, the embedding integration strategy integrates them with feature embedding to obtain the final embedding for community detection. Extensive experiments on ten real-world datasets demonstrate that GDCL outperforms the existing state-of-the-art algorithms.