Mutual Boost Network for attributed graph clustering

Attributed graph clustering is an essential research topic on real-world data. However, the heterogeneous gap between node and structure features limits the existing approaches for discriminative representation. Besides, how to ensure the consistency of clustering assignments produced by the node and structure features is still a challenging problem, which always causes inferior clustering performance. To overcome above problems, a dual-channel network for attributed graph clustering named Mutual Boost Network (MBN) is proposed, which consists of auto-encoder and graph auto-encoder that can interact and learn from each other to achieve mutual boost of clustering performance. First, a novel representation enhancement module is proposed to disseminate the heterogeneous information from both node and structure features for learning comprehensive representation. Then, a consistency constraint by contrasting clustering assignments is devised to provide a mutual guide to make them tend to be consistent. Finally, the procedures of representation learning and clustering assignment are simultaneously optimized under a unified framework in a self-supervised manner. Experimental results on six graph collections show that the MBN performs better than the SOTA approaches by a statistically significant margin. The MBN code can be found at: https://github.com/Xiaoqiang-Yan/MBN.