Graph Joint Representation Clustering via Penalized Graph Contrastive Learning.

Abstract

Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance. In order to alleviate this issue, we propose the idea of maintaining mutual information (MI) between the representations and the inputs to mitigate the loss of semantic information of false negative samples. We demonstrate the validity of this proposal through relevant experiments. Since maximizing MI can be approximately replaced by minimizing reconstruction error, we further propose a graph clustering method based on GCL penalized by reconstruction error, in which our carefully designed reconstruction decoder, as well as reconstruction error term, improve the clustering performance. In addition, we use a pseudo-label-guided strategy to improve the GCL process and further alleviate the problem of false negative samples. Our experiment results demonstrate the superiority and great potential of our proposed graph clustering method compared with state-of-the-art algorithms.