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In recent years, graph contrastive learning (GCL) has emerged as one of theoptimal solutions for various supervised tasks at the node level. However, forunsupervised and structure-related tasks such as community detection, currentGCL algorithms face difficulties in acquiring the necessary community-levelinformation, resulting in poor performance. In addition, general contrastivelearning algorithms improve the performance of downstream tasks by increasingthe number of negative samples, which leads to severe class collision andunfairness of community detection. To address above issues, we propose a novelCommunity-aware Efficient Graph Contrastive Learning Framework (CEGCL) tojointly learn community partition and node representations in an end-to-endmanner. Specifically, we first design a personalized self-training (PeST)strategy for unsupervised scenarios, which enables our model to capture precisecommunity-level personalized information in a graph. With the benefit of thePeST, we alleviate class collision and unfairness without sacrificing theoverall model performance. Furthermore, the aligned graph clustering (AlGC) isemployed to obtain the community partition. In this module, we align theclustering space of our downstream task with that in PeST to achieve moreconsistent node embeddings. Finally, we demonstrate the effectiveness of ourmodel for community detection both theoretically and experimentally. Extensiveexperimental results also show that our CEGCL exhibits state-of-the-artperformance on three benchmark datasets with different scales.

Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training