CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks

Community detection aims to identify dense subgroups of nodes within a network. However, in real-world networks, node attributes are often missing, making traditional methods less effective. In networks with missing attributes, the main challenge of community detection is to deal with the missing attribute information efficiently and use network structure information to make accurate predictions. This article proposes an innovative method called contrastive sampling-aggregating transformer (CSAT) for community detection in attribute-missing networks. CSAT incorporates the contrastive learning principle to capture hidden patterns among nodes and to aggregate information from different samples to create a more robust and accurate methodology for community detection. Specifically, CSAT utilizes a sampling and propagation strategy to obtain different samples and smooth attribute features of the network structure and leverages the Transformer architecture to model the pairwise relationships between nodes. Therefore, our method can address the attribute-missing issue by integrating the auxiliary information from both the network structure and other sources. Extensive experiments on several benchmark datasets demonstrate CSAT's superior performance compared to the state-of-the-art methods for community detection.