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September 20, 2025

Graph Embedded Contrastive Learning for Multi-View Clustering

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This method integrates contrastive learning in multi-view and multi-graph clustering, suggesting improved model performance.

The proposed method improves performance in multi-view clustering and multi-view graph clustering tasks.
By leveraging adaptive graph convolution and autoencoders, it effectively processes multi-view and multi-graph data.
View-cross contrastive learning enhances association accuracy, addressing issues of false associations in sample pairs.
Extensive experiments validate the robustness and superiority of this approach over traditional methods.

He et al. (2025) studied this question.

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