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This paper presents Robust Self-Supervised Symmetric Nonnegative Matrix Factorization (R3SNMF) to enhance graph clustering by addressing the limitations of traditional NMF methods, particularly their sensitivity to noise and inability to capture nonlinear structures. R3SNMF employs a robust principal component model and a self-supervised learning mechanism to iteratively improve clustering quality while preserving network structures. Experimental results demonstrate that R3SNMF outperforms existing clusteri

Author

Said Darya Al Afghani

Language

EN