Finding Adversarially Robust Graph Lottery Tickets
Primary Area: societal considerations including fairness, safety, privacy
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Keywords: Lottery Tickets Hypothesis, Graph Adversarial Attack, Graph Lottery Ticket
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Abstract: Graph Lottery Tickets (GLTs), comprising a sparse graph neural network (GNN) and a sparse input graph adjacency matrix, can significantly reduce the inference latency and compute footprint compared to their dense counterparts. However, their performance against adversarial structure perturbations remains to be fully explored. In this work, we first investigate the resilience of GLTs against different structure perturbation attacks and observe that they are vulnerable and show a large drop in classification accuracy. We then present an adversarially robust graph sparsification (ARGS) framework that prunes the adjacency matrix and the GNN weights by optimizing a novel loss function capturing the graph homophily property and information associated with both 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 adversarially robust graph lottery tickets that are highly sparse yet achieve competitive performance under different untargeted training-time structure attacks. Evaluations conducted on various benchmarks, considering different poisoning structure attacks such as PGD, MetaAttack, PR-BCD attack, and adaptive attacks, demonstrate that the GLTs generated by ARGS can significantly improve their robustness, even when subjected to high levels of sparsity.
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Submission Number: 1009