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.

Whenthe smooth, weobtain: Problem (3.6) ourand Assumption 2.2 holds.

Con set isa (scaled)`p ball, 1 p< 2, then, considering(2), the thebestmethod p d/logdtimeslar weprovidene . Formally, if 2 then s 2 { 1}d implies (sj j)j d 2 .

Our4 results certify that there exists an optimal linear pre-conditioner for quadratically convex constraint sets. As such,5 adaptivegradient methods can be minimax (rate) optimal. Inonline algorithms, the common practice [4,5,6,7,2]6 is to measure regret with respect to the "best" post-hoc regularizer (i.e. In this setting, the constraint set corresponds to the set of classifiers of interest, and the geometry of the gradients34 corresponds tothegeometry ofthefeatures (orcovariates). A generalized online mirror descent with applications to classification and52 regression.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/