AI RESEARCH
MoEGCL: Mixture of Ego-Graphs Contrastive Representation Learning for Multi-View Clustering
arXiv CS.LG
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ArXi:2511.05876v5 Announce Type: replace-cross In recent years, the advancement of Graph Neural Networks (GNNs) has significantly propelled progress in Multi-View Clustering (MVC). However, existing methods face the problem of coarse-grained graph fusion. Specifically, current approaches typically generate a separate graph structure for each view and then perform weighted fusion of graph structures at the view level, which is a relatively rough strategy. To address this limitation, we present a novel Mixture of Ego-Graphs Contrastive Representation Learning (Mo