Resilient Graph Neural Networks: A Coupled Dynamical Systems Approach
This addresses the issue of adversarial robustness in GNNs for applications in graph-based tasks, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of Graph Neural Networks (GNNs) being vulnerable to adversarial attacks by introducing a method based on coupled dynamical systems with contractive properties, which improves robustness and achieves performance on par or better than existing methods on real-world benchmarks.
Graph Neural Networks (GNNs) have established themselves as a key component in addressing diverse graph-based tasks. Despite their notable successes, GNNs remain susceptible to input perturbations in the form of adversarial attacks. This paper introduces an innovative approach to fortify GNNs against adversarial perturbations through the lens of coupled dynamical systems. Our method introduces graph neural layers based on differential equations with contractive properties, which, as we show, improve the robustness of GNNs. A distinctive feature of the proposed approach is the simultaneous learned evolution of both the node features and the adjacency matrix, yielding an intrinsic enhancement of model robustness to perturbations in the input features and the connectivity of the graph. We mathematically derive the underpinnings of our novel architecture and provide theoretical insights to reason about its expected behavior. We demonstrate the efficacy of our method through numerous real-world benchmarks, reading on par or improved performance compared to existing methods.