Incorporating Higher-order Structural Information for Graph Clustering

arXiv:2403.11087v1 Announce Type: new
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 …

abstract arxiv clustering cs.lg cs.si data data mining graph however information mining network node nodes significance tool type