Deep Graph Clustering via Mutual Information Maximization and Mixture Model
This addresses the problem of community detection in graphs for researchers and practitioners, but it is incremental as it builds on existing graph contrastive learning methods.
The paper tackles attributed graph clustering by proposing a contrastive learning framework that jointly optimizes node embeddings and a mixture of Gaussians model, achieving effective community detection on real-world datasets.
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.