Multi-view Contrastive Graph Clustering

• URL: http://arxiv.org/abs/2110.11842v1
• Date: Fri, 22 Oct 2021 15:22:42 GMT
• Title: Multi-view Contrastive Graph Clustering
• Authors: Erlin Pan, Zhao Kang
• Abstract summary: We propose a generic framework to cluster multi-view attributed graph data. Inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method. Our simple approach outperforms existing deep learning-based methods.
• Score: 12.463334005083379
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attributed graph data. Specifically, inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method to learn a consensus graph since the original graph could be noisy or incomplete and is not directly applicable. Our method composes of two key steps: we first filter out the undesirable high-frequency noise while preserving the graph geometric features via graph filtering and obtain a smooth representation of nodes; we then learn a consensus graph regularized by graph contrastive loss. Results on several benchmark datasets show the superiority of our method with respect to state-of-the-art approaches. In particular, our simple approach outperforms existing deep learning-based methods.

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