Clustering Information-guided Multi-view Contrastive Graph Clustering

Nowadays some progress has been made in learning the representation of nodes in unlabeled multi-view for clustering. However, current multi-view graph clustering methods ignore important clustering information during training leading to limited performance. In this paper, we introduce a new framework for multi-view graph clustering, known as IGMC. It utilizes both node attributes and graph topology and uses network topology and the pseudo-labels obtained during the training process as supervisory signals to construct pairs of samples with high discriminative ability and high reliability so that each node corresponds to multiple positive samples. In addition, the clustering quality of the node representations is improved by maximizing the mutual information between the node and the corresponding clusters to emphasize the classes of the nodes. IGMC produces better results than advanced methods on several challenging datasets. The source code is available at https://github.com/tczgithub/IGMC.