Deep Graph Clustering via Mutual Information Maximization and Mixture Model

Abstract

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.

References in corpus (9)

• Finding and evaluating community structure in networks
• DeepWalk: Online Learning of Social Representations
• Finding community structure in networks using the eigenvectors of matrices
• Stochastic blockmodels and community structure in networks
• Community Detection in Networks with Node Attributes
• Learning Graph Embedding with Adversarial Training Methods
• Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
• Community Structure of the Physical Review Citation Network
• Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding