Incorporating Higher-order Structural Information for Graph Clustering
<|reference_start|>Incorporating Higher-order Structural Information for Graph Clustering: 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.<|reference_end|>
@article{li2024incorporating,
    title={Incorporating Higher-order Structural Information for Graph Clustering},
    author={Qiankun Li, Haobing Liu, Ruobing Jiang, and Tingting Wang},
    journal={DASFAA 2024},
    year={2024},
    doi={10.1007/978-981-97-5562-2_34},
    archivePrefix={arXiv},
    eprint={2403.11087},
    primaryClass={cs.LG cs.SI}
}