Deep Graph Clustering via Mutual Information Maximization and Mixture Model [article]

Maedeh Ahmadi, Mehran Safayani, Abdolreza Mirzaei
2022 arXiv   pre-print
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
more » ... 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.
arXiv:2205.05168v1 fatcat:o6xb72h5fvhp7kp5wbwqwcmeqy