Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective



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Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification. However, only few work has addressed the adversarial robustness of GNNs. In this paper, we first present a novel gradient-based attack method that facilitates the difficulty of tackling discrete graph data. When comparing to current adversarial attacks on GNNs, the results show that by only perturbing a small number of edge perturbations, including addition and deletion, our optimization-based attack can lead to a noticeable decrease in classification performance. Moreover, leveraging our gradient-based attack, we propose the first optimization-based adversarial training for GNNs. Our method yields higher robustness against both different gradient based and greedy attack methods without sacrifice classification accuracy on original graph.

Please cite our work using the BibTeX below.

  title     = {Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective},
  author    = {Xu, Kaidi and Chen, Hongge and Liu, Sijia and Chen, Pin-Yu and Weng, Tsui-Wei and Hong, Mingyi and Lin, Xue},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {3961--3967},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/550},
  url       = {},
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