CLUHCS: Dual-View Contrastive Learning Enabled Unsupervised Heterogeneous Community Search with Meta-Path Behavior Modeling

Existing community search methods heavily rely on labeled data or predefined structures, thus fail to capture obscure and dynamic community boundaries in open-world heterogeneous networks, leading to poor adaptability. They also ignore modeling behavioral patterns, resulting in poor search performance. To solve the above issues, this work formally defines the unsupervised behavior-driven community search problem for heterogeneous graphs and designs dual-view Contrastive Learning-based Unsupervised framework for Heterogeneous graph Community Search (CLUHCS). CLUHCS designs a relation view to encode local community cohesion and a meta-path view to capture global behavior semantics. By using PathSim averaging strategy to generate positive samples and self-supervised signals, we can completely eliminate label dependency. Then, contrastive training is leveraged to automatically learn community representations and solve the open community boundary ambiguity challenge. Furthermore, by capturing behavior patterns, the meta-path behavior modeling flexibly characterizes the formation mechanism of heterogeneous communities. Experiments on three datasets verify the effectiveness and efficiency of CLUHCS. CLUHCS significantly improves F1-score by 52.7% over the supervised baseline FCS-HGNN and by 41.5% over the unsupervised method TransZero.