Finding Adversarially Robust Graph Lottery Tickets

Subhajit Dutta Chowdhury, Zhiyu Ni, Qingyuan Peng, Souvik Kundu, Pierluigi Nuzzo

Abstract: Graph lottery tickets (GLTs), comprising a sparse graph neural network (GNN) and a sparse input graph adjacency matrix, can significantly reduce the computing footprint of inference tasks compared to their dense counterparts. However, their performance against adversarial attacks remains to be fully explored. In this paper, we first investigate the resilience of GLTs against different {poisoning attacks based on structure perturbations} and observe that they are vulnerable and show a large drop in classification accuracy. We then present an \emph{adversarially robust graph sparsification (ARGS)} framework that prunes the adjacency matrix and the GNN weights by minimizing a novel loss function capturing the graph homophily property and information associated with the true labels of the train nodes and the pseudo labels of the test nodes. By iteratively applying ARGS to prune both the perturbed graph adjacency matrix and the GNN model weights, we can find graph lottery tickets that are highly sparse yet achieve competitive performance under different training-time (poisoning) structure-perturbation attacks. Evaluations conducted on various benchmarks, considering {attacks} such as PGD, MetaAttack, PR-BCD, GR-BCD, and adaptive attack, demonstrate that ARGS can significantly improve the robustness of the generated GLTs, even when subjected to high levels of sparsity.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Sanghyun_Hong1

Submission Number: 2531