GLAC-GCN: Global and Local Topology-Aware Contrastive Graph Clustering Network

Though many deep attributed graph clustering approaches have been developed in recent years, most still suffer from two limitations. First, in the input space, they primarily rely on the original topology structure as the input (to some graph network), lacking the ability to jointly leverage local and global topology information to refine the graph. Second, in the learning process, they usually employ a single graph learning pipeline (with a single input graph), overlooking the opportunities in the joint optimization of multiple graph learning pipelines (with multiple topology structures). In view of this, this article presents a global and local topology-aware contrastive graph clustering network (GLAC-GCN) for attributed graph clustering. Specifically, the local topology structure, and global semantic information are simultaneously utilized to refine the graph. Then a paralleled graph convolutional network (GCN) learning mechanism is designed, where i) both the original graph and the globally and locally refined graph are treated as input graphs, and ii) two pipelines of GCNs are jointly and interactively utilized. Furthermore, a self-adaptive learning mechanism is devised to ensure consistency between multiple learning pipelines via the Kullback–Leibler (KL)-divergence. Meanwhile, the contrastive learning is enforced by minimizing the mismatch of the cluster distributions obtained from different GCN pipelines. Extensive experiments are conducted on seven real-world datasets. Notably, GLAC-GCN achieves the best ACC (or NMI) scores on all (or five) of the seven datasets, demonstrating its superiority over the state-of-the-art approaches. Code available: https://github.com/xuyuankun631/GLAC-GCN.