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Our analysis of the generalization error is based on an extension of Gordon's Gaussian process inequality [ R is a continuous function, which is convex in the first argument and concave in the second argument. The main result of CGMT is to connect the above two random optimization problems. The CGMT framework has been used to infer statistical properties of estimators in certain high-dimensional asymptotic regime. Second, derive the point-wise limit of the AO objective in terms of a convex-concave optimization problem, over only few scalar variables.

Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nodes into the original graph and pose realistic threats.