Edge Dithering for Robust Adaptive Graph Convolutional Networks

Abstract--Graph convolutional networks (GCNs) are vulnerable to perturbations of the graph structure that are either random, or, adversarially designed. The perturbed links mo dify the graph neighborhoods, which critically affects the perf ormance of GCNs in semi-supervised learning (SSL) tasks. Aiming at robustifying GCNs conditioned on the perturbed graph, the present paper generates multiple auxiliary graphs, each ha ving its binary 0 1 edge weights flip values with probabilities designed to enhance robustness. The resultant edge-dither ed auxiliary graphs are leveraged by an adaptive (A)GCN that performs SSL. Robustness is enabled through learnable grap h-combining weights along with suitable regularizers. Relat ive to GCN, the novel AGCN achieves markedly improved performance in tests with noisy inputs, graph perturbations, and state-of- the-art adversarial attacks. A task of major importance at the crossroads of machine learning and network science is semi-supervised learning (SSL) over graphs.