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Self-supervised Contrastive Attributed Graph Clustering
[article]
2021
arXiv
pre-print
Attributed graph clustering, which learns node representation from node attribute and topological graph for clustering, is a fundamental but challenging task for graph analysis. Recently, methods based on graph contrastive learning (GCL) have obtained impressive clustering performance on this task. Yet, we observe that existing GCL-based methods 1) fail to benefit from imprecise clustering labels; 2) require a post-processing operation to get clustering labels; 3) cannot solve out-of-sample
arXiv:2110.08264v1
fatcat:gcdfo4ulhbhj3ax6e3obpwoxqu