Transferring robustness for graph neural network against poisoning attacks

Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce the performances of GNNs. It is very challenging to design robust graph neural networks against poisoning attack and several efforts have been taken. Existing work aims at reducing the negative impact from adversarial edges only with the poisoned graph, which is sub-optimal since they fail to discriminate adversarial edges from normal ones. On the other hand, clean graphs from similar domains as the target poisoned graph are usually available in the real world. By perturbing these clean graphs, we create supervised knowledge to train the ability to detect adversarial edges so that the robustness of GNNs is elevated. However, such potential for clean graphs is neglected by existing work. To this end, we investigate a novel problem of improving the robustness of GNNs against poisoning attacks by exploring clean graphs. Specifically, we propose PA-GNN, which relies on a penalized aggregation mechanism that directly restrict the negative impact of adversarial edges by assigning them lower attention coefficients. To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph. Experimental results on four real-world datasets demonstrate the robustness of PA-GNN against poisoning attacks on graphs.

Original languageEnglish (US)
Title of host publicationWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages600-608
Number of pages9
ISBN (Electronic)9781450368223
DOIs
StatePublished - Jan 20 2020
Event13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States
Duration: Feb 3 2020Feb 7 2020

Publication series

NameWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

Conference

Conference13th ACM International Conference on Web Search and Data Mining, WSDM 2020
CountryUnited States
CityHouston
Period2/3/202/7/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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  • Cite this

    Tang, X., Li, Y., Sun, Y., Yao, H., Mitra, P., & Wang, S. (2020). Transferring robustness for graph neural network against poisoning attacks. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 600-608). (WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3336191.3371851