Certifiable Robustness to Graph Perturbations

TL;DR

This paper introduces a novel method for certifying the robustness of graph neural networks against structural and attribute perturbations, leveraging connections to PageRank and Markov decision processes, and proposes training procedures to enhance robustness.

Contribution

It presents the first verification method for certifiable robustness in graph models, applicable to a broad class including GNNs, with efficient computation and robust training strategies.

Findings

Certificates can be efficiently computed for various threat models.

Robust training increases the number of certifiably robust nodes.

The method maintains or improves predictive accuracy while enhancing robustness.

Abstract

Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showing that they are extremely vulnerable to adversarial attacks on both the graph structure and the node attributes. We propose the first method for verifying certifiable (non-)robustness to graph perturbations for a general class of models that includes graph neural networks and label/feature propagation. By exploiting connections to PageRank and Markov decision processes our certificates can be efficiently (and under many threat models exactly) computed. Furthermore, we investigate robust training procedures that increase the number of certifiably robust nodes while maintaining or improving the clean predictive accuracy.