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Deep Graph Clustering via Mutual Information Maximization and Mixture Model
[article]
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
arXiv:2205.05168v1
fatcat:o6xb72h5fvhp7kp5wbwqwcmeqy