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University of Rochester
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Covariance-based GNNs are fundamentally learnable generalizations of PCA, offering rigorous stability bounds against finite-sample noise that standard graph theory fails to capture.
Stability-promoting regularization in flow models can significantly enhance robustness to structural noise in graph signal generation without compromising output quality.
