Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation
This work addresses vulnerabilities in GNNs for secure applications, but it is incremental as it builds on existing adversarial attack research.
The paper tackles the problem of adversarial attacks on graph neural networks (GNNs) by investigating edge perturbations at test time, finding that inserting edges between nodes of different classes has a higher impact than removing edges within the same class.
Despite the success of graph neural networks (GNNs) in various domains, they exhibit susceptibility to adversarial attacks. Understanding these vulnerabilities is crucial for developing robust and secure applications. In this paper, we investigate the impact of test time adversarial attacks through edge perturbations which involve both edge insertions and deletions. A novel explainability-based method is proposed to identify important nodes in the graph and perform edge perturbation between these nodes. The proposed method is tested for node classification with three different architectures and datasets. The results suggest that introducing edges between nodes of different classes has higher impact as compared to removing edges among nodes within the same class.