Multi-dimensional Topological Association Strengthening Clustering Network

Mengzhe Sun, Renda Han, Moxuan Zeng, Mengfei Li, Tianyu Hu, Zhenhua Yang, Ziyan Wang, Jingxin Liu, Wen Xin, Jingmei Feng

Abstract: Deep graph clustering, as a fundamental task in data mining, has attracted widespread attention. Recently, excellent performance has been achieved by integrating graph structure and node attributes to generate consensus latent embeddings. However, existing clustering methods are limited by redundant information and unreliable clustering distribution, which hinders the discriminative power of the latent embeddings. To address this issue, we propose a novel deep graph clustering framework called Multi-dimensional Topological Association Strengthening Clustering Network (MTASCN). Specifically, we design a Multi-dimensional Feature Association Mechanism (MFAM), which extracts the competitive or cooperative relationship between features to alleviate the interference of redundant features and enhance the dominant features. In addition, we develop a Structure-oriented Multi-order Loss Module (SMLM) that reinforces the generation of clustering distribution under reliable structure information guidance by calculating the multi-order similarity between the latent embeddings and the original graph structure. Extensive experiments on five benchmark datasets have demonstrated that MTASCN consistently outperforms other clustering methods.