Hierarchical Attacks on Large-Scale Graph Neural Networks

In this paper, we present a novel hierarchical approach to adversarial attacks targeting Graph Neural Networks (GNNs), tailored to overcome the complexities inherent in large-scale poisoning attacks. Traditional global attack strategies often fail to yield effective results on extensive graph structures. Our innovative method implements a divide-and-conquer tactic, clustering nodes based on their embeddings and forming coarse-grained graphs from these clusters. We initiate perturbations at this coarse level, gradually honing them in more detailed, finer-grained graphs, while keeping non-essential nodes grouped. By employing meta-gradients derived from these refined graphs, we pinpoint critical edges for perturbation, thereby vastly simplifying the process and reducing the intricacy involved in manipulating large-scale graphs. This hierarchical strategy not only enhances the efficacy of the attacks but also maintains operational efficiency across expansive network structures.