Certifiable Robustness of Graph Convolutional Networks Under Structure Perturbation

MCML Authors

Daniel Zügner

Dr.

* Former Member

→ Group Stephan Günnemann
 Data Analytics & Machine Learning

Stephan Günnemann

Prof. Dr.

Principal Investigator

Data Analytics & Machine Learning

Abstract

Recent works show that message-passing neural networks (MPNNs) can be fooled by adversarial attacks on both the node attributes and the graph structure. Since MPNNs are currently being rapidly adopted in real-world applications, it is thus crucial to improve their reliablility and robustness. While there has been progress on robustness certification of MPNNs under perturbation of the node attributes, no existing method can handle structural perturbations. These perturbations are especially challenging because they alter the message passing scheme itself. In this work we close this gap and propose the first method to certify robustness of Graph Convolutional Networks (GCNs) under perturbations of the graph structure. We show how this problem can be expressed as a jointly constrained bilinear program - a challenging, yet well-studied class of problems - and propose a novel branch-and-bound algorithm to obtain lower bounds on the global optimum. These lower bounds are significantly tighter and can certify up to twice as many nodes compared to a standard linear relaxation.

inproceedings

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KDD 2020

26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California, USA, Aug 23-27, 2020.

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Authors

D. Zügner • S. Günnemann

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Links

DOI

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Research Area

 A3 | Computational Models