Structure-enhanced Contrastive Learning for Graph Clustering

• URL: http://arxiv.org/abs/2408.09790v1
• Date: Mon, 19 Aug 2024 08:39:08 GMT
• Title: Structure-enhanced Contrastive Learning for Graph Clustering
• Authors: Xunlian Wu, Jingqi Hu, Anqi Zhang, Yining Quan, Qiguang Miao, Peng Gang Sun,
• Abstract summary: Structure-enhanced Contrastive Learning (SECL) is introduced to addresses issues by leveraging inherent network structures. SECL utilizes a cross-view contrastive learning mechanism to enhance node embeddings without elaborate data augmentations. Extensive experiments on six datasets confirm SECL's superiority over current state-of-the-art methods.
• Score: 4.6746630466993055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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