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.

提出了一种新颖的图聚类网络，能充分利用图结构信息，捕获高阶结构信息，并应用三元自监督模块作为结构约束，在多个数据集上优于多个最先进的方法，显示出其优越性。