Incorporating Higher-order Structural Information for Graph Clustering

• URL: http://arxiv.org/abs/2403.11087v2
• Date: Tue, 19 Mar 2024 13:37:36 GMT
• Title: Incorporating Higher-order Structural Information for Graph Clustering
• Authors: Qiankun Li, Haobing Liu, Ruobing Jiang, Tingting Wang,
• Abstract summary: Graph convolutional network (GCN) has emerged as a powerful tool for deep clustering. We propose a novel graph clustering network to make full use of graph structural information. Our proposed model outperforms many state-of-the-art methods on various datasets.
• Score: 6.027366081402081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering holds profound significance in data mining. In recent years, graph convolutional network (GCN) has emerged as a powerful tool for deep clustering, integrating both graph structural information and node attributes. However, most existing methods ignore the higher-order structural information of the graph. Evidently, nodes within the same cluster can establish distant connections. Besides, recent deep clustering methods usually apply a self-supervised module to monitor the training process of their model, focusing solely on node attributes without paying attention to graph structure. In this paper, we propose a novel graph clustering network to make full use of graph structural information. To capture the higher-order structural information, we design a graph mutual infomax module, effectively maximizing mutual information between graph-level and node-level representations, and employ a trinary self-supervised module that includes modularity as a structural constraint. Our proposed model outperforms many state-of-the-art methods on various datasets, demonstrating its superiority.

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