  Attributed graph clustering or community detection which learns to cluster
the nodes of a graph is a challenging task in graph analysis. In this paper, we
introduce a contrastive learning framework for learning clustering-friendly
node embedding. Although graph contrastive learning has shown outstanding
performance in self-supervised graph learning, using it for graph clustering is
not well explored. We propose Gaussian mixture information maximization (GMIM)
which utilizes a mutual information maximization approach for node embedding.
Meanwhile, it assumes that the representation space follows a Mixture of
Gaussians (MoG) distribution. The clustering part of our objective tries to fit
a Gaussian distribution to each community. The node embedding is jointly
optimized with the parameters of MoG in a unified framework. Experiments on
real-world datasets demonstrate the effectiveness of our method in community
detection.
