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Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns

Authors:

Daniel Zügner,

Oliver Borchert,

Amir Akbarnejad,

Stephan GünnemannAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 5

Article No.: 57, Pages 1 - 31

https://doi.org/10.1145/3394520

Published: 21 June 2020 Publication History

Abstract

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g., the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we present a study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node’s features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain, we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given. For the first time, we successfully identify important patterns of adversarial attacks on graph neural networks (GNNs) — a first step towards being able to detect adversarial attacks on GNNs.

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Cited By

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Zhu YMandulak MWu KSlota GJeon YChow KYu LPintor MChen XJagielski M(2024)On the Robustness of Graph Reduction Against GNN BackdoorProceedings of the 2024 Workshop on Artificial Intelligence and Security10.1145/3689932.3694762(65-76)Online publication date: 6-Nov-2024
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Index Terms

Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 5

Special Issue on KDD 2018, Regular Papers and Survey Paper

October 2020

376 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3407672

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 March 2020

Received: 01 March 2019

Published in TKDD Volume 14, Issue 5

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Technical University of Munich - Institute for Advanced Study
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Li YQi SWang XZhang JCui L(2024)A Novel Tree-Based Method for Interpretable Reinforcement LearningACM Transactions on Knowledge Discovery from Data10.1145/369546418:9(1-22)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3695464
Zhu YMandulak MWu KSlota GJeon YChow KYu LPintor MChen XJagielski M(2024)On the Robustness of Graph Reduction Against GNN BackdoorProceedings of the 2024 Workshop on Artificial Intelligence and Security10.1145/3689932.3694762(65-76)Online publication date: 6-Nov-2024
https://dl.acm.org/doi/10.1145/3689932.3694762
Zhang LXue MZhang LZhang YLiu W(2024)An Imperceptible and Owner-unique Watermarking Method for Graph Neural NetworksProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674443(108-113)Online publication date: 5-Jul-2024
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