  Attributed graph clustering, which learns node representation from node
attribute and topological graph for clustering, is a fundamental but
challenging task for graph analysis. Recently, methods based on graph
contrastive learning (GCL) have obtained impressive clustering performance on
this task. Yet, we observe that existing GCL-based methods 1) fail to benefit
from imprecise clustering labels; 2) require a post-processing operation to get
clustering labels; 3) cannot solve out-of-sample (OOS) problem. To address
these issues, we propose a novel attributed graph clustering network, namely
Self-supervised Contrastive Attributed Graph Clustering (SCAGC). In SCAGC, by
leveraging inaccurate clustering labels, a self-supervised contrastive loss,
which aims to maximize the similarities of intra-cluster nodes while minimizing
the similarities of inter-cluster nodes, are designed for node representation
learning. Meanwhile, a clustering module is built to directly output clustering
labels by contrasting the representation of different clusters. Thus, for the
OOS nodes, SCAGC can directly calculate their clustering labels. Extensive
experimental results on four benchmark datasets have shown that SCAGC
consistently outperforms 11 competitive clustering methods.
