Defending Graph Neural Networks against Adversarial Attacks

GNNGuard is a model-agnostic approach that can defend any Graph Neural Network against a variety of adversarial attacks.

Deep learning methods for graphs achieve remarkable performance on many tasks. However, despite the proliferation of such methods and their success, recent findings indicate that even the strongest and most popular Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks. Adversarial attacks mean that an attacker injects small but carefully-designed perturbations to the graph structures in order to degrade the performance of GNN classifiers.

The vulnerability is a significant issue preventing GNNs from being used in real-world applications. For example, under adversarial attack, small and unnoticeable perturbations of graph structure (e.g., adding two edges on the poisoned node) can catastrophically reduce performance (panel A in the figure).

We develop GNNGuard, a general algorithm to defend against a variety of training-time attacks that perturb the discrete graph structure. GNNGuard can be straightforwardly incorporated into any GNN. By integrating GNNGuard, the GNN classifier can make correct predictions even when trained on the attacked graph (panel B in the figure).

GNNGuard algorithm

Most damaging attacks add fake edges between nodes that have different features and labels. Because of that, the key idea of GNNGuard is to detect and quantify the relationship between the graph structure and node features, if one exists, and then exploit that relationship to mitigate negative effects of the attack. GNNGuard learns how to best assign higher weights to edges connecting similar nodes while pruning edges between unrelated nodes. In specific, instead of the neural message passing of a typical GNN (panel A in the figure), GNNGuard (panel B in the figure) controls the message stream, such as blocking the message from irrelevant neighbors while strengthening messages from highly-related ones.

Remarkably, GNNGuard can effectively restore state-of-the-art performance of GNNs in the face of various adversarial attacks, including targeted and non-targeted attacks, and can defend against attacks on both homophily and heterophily graphs.

Attractive properties of GNNGuard

  • Defense against a variety of attacks: GNNGuard is a general defense approach that is effective against a variety of training-time attacks, including directly targeted, influence, and non-targeted attacks.
  • Integrates with any GNNs: GNNGuard can defend any modern GNN architecture against adversarial attacks.
  • State-of-the-art performance on clean graphs: In real-world settings, we do not know whether a graph has been attacked or not. GNNGuard can restore state-of-the-art performance of a GNN when the graph is attached as well as sustain the original performance on non-attacked graphs.
  • Homophily and heterophily graphs: GNNGuard is the first technique that can defend GNNs against attacks on homophily and heterophily graphs. GNNGuard can be easily generalized to graphs with abundant structural equivalences, where connected nodes have different node features yet similar structural roles.


GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Xiang Zhang and Marinka Zitnik
NeurIPS 2020 [arXiv] [poster]

title     = {GNNGuard: Defending Graph Neural Networks against Adversarial Attacks},
author    = {Zhang, Xiang and Zitnik, Marinka},
booktitle = {Proceedings of Neural Information Processing Systems, NeurIPS},
year      = {2020}

Code and datasets

Pytorch implementation of GNNGuard and all datasets are available in the GitHub repository.


Latest News

Sep 2023:   New papers accepted at NeurIPS

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers, which are among the top 3% of all submissions, focus on explaining sequence models through self-supervised learning and the full-atom design of protein pockets.

Sep 2023:   Future Directions in Network Biology

Excited to share our perspectives on current and future directions in network biology.

Aug 2023:   Scientific Discovery in the Age of AI

Jul 2023:   PINNACLE - Contextual AI protein model

PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

Jun 2023:   Our Group is Joining the Kempner Institute

Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

Jun 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.

Jun 2023:   On Pretraining in Nature Machine Intelligence

May 2023:   Congratulations to Ada and Michelle

Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

Apr 2023:   Universal Domain Adaptation at ICML 2023

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

Apr 2023:   Celebrating Achievements of Our Undergrads

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

Apr 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

Apr 2023:   New Study in Nature Machine Intelligence

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

Mar 2023:   Precision Health in Nature Machine Intelligence

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

Mar 2023:   GraphXAI published in Scientific Data

Feb 2023:   Welcoming New Postdoctoral Fellows

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

Feb 2023:   New Preprint on Distribution Shifts

Feb 2023:   PrimeKG published in Scientific Data

Jan 2023:   GNNDelete published at ICLR 2023

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics