Related papers: Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation

Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation

URL: http://arxiv.org/abs/2312.17301v1
Date: Thu, 28 Dec 2023 17:41:30 GMT
Title: Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation
Authors: Dibaloke Chanda, Saba Heidari Gheshlaghi and Nasim Yahya Soltani
Abstract summary: 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. Results suggest that introducing edges between nodes of different classes has higher impact as compared to removing edges among nodes within the same class.
Score: 1.6385815610837167
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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