Deep graph clustering via aligning representation learning

Deep graph clustering is a fundamental yet challenging task for graph data analysis. Recent efforts have witnessed significant success in combining autoencoder and graph convolutional network to explore graph-structured data. However, we observe that these approaches tend to map different nodes into the same representation, thus resulting in less discriminative node feature representation and limited clustering performance. Although some contrastive graph clustering methods alleviate the problem, they heavily depend on the carefully selected data augmentations, which greatly limits the capability of contrastive learning. Otherwise, they fail to consider the self-consistency between node representations and cluster assignments, thus affecting the clustering performance. To solve these issues, we propose a novel contrastive deep graph clustering method termed Aligning Representation Learning Network (ARLN). Specifically, we utilize contrastive learning between an autoencoder and a graph autoencoder to avoid conducting complex data augmentations. Moreover, we introduce an instance contrastive module and a feature contrastive module for consensus representation learning. Such modules are able to learn a discriminative node representation via contrastive learning. In addition, we design a novel assignment probability contrastive module to maintain the self-consistency between node representations and cluster assignments. Extensive experimental results on three benchmark datasets show the superiority of the proposed ARLN against the existing state-of-the-art deep graph clustering methods.