CGAF: Community-Guided Attentive Fusion for Community Detection in Multiplex Networks

Abstract

Community detection in multiplex networks seeks groups of nodes from several types of relations. Attention mechanisms can integrate these relations, but weights based on embedding compatibility do not explicitly measure the community support provided by a node’s neighborhood. We propose Community-Guided Attentive Fusion (CGAF), an end-to-end graph neural network framework that uses neighborhood community evidence to guide relation fusion. Graph contrastive learning encodes refined relation graphs and a learned fused graph to capture relation-specific and shared information. Shared prototypes produce provisional soft memberships, which are used to calculate degree-corrected neighborhood evidence. This evidence adjusts node-specific attention, and a learnable residual coefficient combines the relation embeddings with the fused-graph representation. The prototypes then assign communities to the resulting representations. Joint optimization with contrastive, self-training, and modularity objectives connects representation learning, relation weighting, and community assignment. On ACM, DBLP, and Yelp, CGAF achieves the highest observed means across four community detection metrics among eight comparison methods under the stated protocols. Ablation and parameter analyses show how community evidence, structural supervision, and residual fusion contribute to the learned partitions.