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arXiv · 2009.05872

Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing

Zhidong Gao·Rui Hu·Yanmin Gong
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Abstract

Graph classification has practical applications in diverse fields. Recent studies show that graph-based machine learning models are especially vulnerable to adversarial perturbations due to the non i.i.d nature of graph data. By adding or deleting a small number of edges in the graph, adversaries could greatly change the graph label predicted by a graph classification model. In this work, we propose to build a smoothed graph classification model with certified robustness guarantee. We have proven that the resulting graph classification model would output the same prediction for a graph under $l_0$ bounded adversarial perturbation. We also evaluate the effectiveness of our approach under graph convolutional network (GCN) based multi-class graph classification model.

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Zhidong Gao, Rui Hu, Yanmin Gong. 2020-09-12. Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing. https://arxiv.org/abs/2009.05872

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