Scapin: Scalable Graph Structure Perturbation by Augmented Influence Maximization

Abstract

Generating data perturbations to graphs has become a useful tool for analyzing the robustness of Graph Neural Networks (GNNs). However, existing model-driven methodologies can be prohibitively expensive to apply in large graphs, which hinders the understanding of GNN robustness at scale. In this paper, we present Scapin, a data-driven methodology that opens up a new perspective by connecting graph structure perturbation for GNNs with augmented influence maximization-to either facilitate desirable spreads or curtail undesirable ones by adding or deleting a small set of edges. This connection not only allows us to perform data perturbation on GNNs with computation scalability but also provides nice interpretations. To transform such connections into efficient perturbation approaches for the new GNN setting, Scapin introduces a novel edge influence model, decomposed influence maximization objectives, and a principled algorithm for edge addition by exploiting submodularity of the objectives. Empirical studies demonstrate that Scapin can give orders of magnitude improvement over state-of-art methods in terms of runtime and memory efficiency, with comparable or even better performance.