Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Si-Yu Yi Wei Ju Yifang Qin Xiao Luo Luchen Liu Yong-Dao Zhou Ming Zhang
Graph clustering, which learns the node representations for effective cluster
assignments, is a fundamental yet challenging task in data analysis and has
received considerable attention accompanied by graph neural networks in recent
years. However, most existing methods overlook the inherent relational
information among the non-independent and non-identically distributed nodes in
a graph. Due to the lack of exploration of relational attributes, the semantic
information of the graph-structured data fails to be fully exploited which
leads to poor clustering performance. In this paper, we propose a novel
self-supervised deep graph clustering method named Relational Redundancy-Free
Graph Clustering (R$^2$FGC) to tackle the problem. It extracts the attribute-
and structure-level relational information from both global and local views
based on an autoencoder and a graph autoencoder. To obtain effective
representations of the semantic information, we preserve the consistent
relation among augmented nodes, whereas the redundant relation is further
reduced for learning discriminative embeddings. In addition, a simple yet valid
strategy is utilized to alleviate the over-smoothing issue. Extensive
experiments are performed on widely used benchmark datasets to validate the
superiority of our R$^2$FGC over state-of-the-art baselines. Our codes are
available at https://github.com/yisiyu95/R2FGC.