Structure-enhanced Contrastive Learning for Graph Clustering
<|reference_start|>Structure-enhanced Contrastive Learning for Graph Clustering: Graph clustering is a crucial task in network analysis with widespread applications, focusing on partitioning nodes into distinct groups with stronger intra-group connections than inter-group ones. Recently, contrastive learning has achieved significant progress in graph clustering. However, most methods suffer from the following issues: 1) an over-reliance on meticulously designed data augmentation strategies, which can undermine the potential of contrastive learning. 2) overlooking cluster-oriented structural information, particularly the higher-order cluster(community) structure information, which could unveil the mesoscopic cluster structure information of the network. In this study, Structure-enhanced Contrastive Learning (SECL) is introduced to addresses these issues by leveraging inherent network structures. SECL utilizes a cross-view contrastive learning mechanism to enhance node embeddings without elaborate data augmentations, a structural contrastive learning module for ensuring structural consistency, and a modularity maximization strategy for harnessing clustering-oriented information. This comprehensive approach results in robust node representations that greatly enhance clustering performance. Extensive experiments on six datasets confirm SECL's superiority over current state-of-the-art methods, indicating a substantial improvement in the domain of graph clustering.<|reference_end|>
@article{wu2024structure-enhanced,
    title={Structure-enhanced Contrastive Learning for Graph Clustering},
    author={Xunlian Wu, Jingqi Hu, Anqi Zhang, Yining Quan, Qiguang Miao, Peng
  Gang Sun},
    journal={arXiv preprint arXiv:2408.09790},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09790},
    primaryClass={cs.LG}
}