@inproceedings{
chowdhury2023sparse,
title={Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets},
author={Subhajit Dutta Chowdhury and Zhiyu Ni and Qingyuan Peng and Souvik Kundu and Pierluigi Nuzzo},
booktitle={NeurIPS 2023 Workshop: New Frontiers in Graph Learning},
year={2023},
url={https://openreview.net/forum?id=VAATRBCyPg}
}
Graph Lottery Tickets (GLTs), comprising a sparse adjacency matrix and a sparse graph neural network (GNN), can significantly reduce the inference latency and compute footprint compared to their dense counterparts. Despite these benefits, 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 highly vulnerable and show a large drop in classification accuracy. Based on this observation, 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, namely, PGD, MetaAttack, Meta-PGD, and PR-BCD demonstrate that the GLTs generated by ARGS can significantly improve the robustness, even when subjected to high levels of sparsity.
Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets
Subhajit Dutta Chowdhury Zhiyu Ni Qingyuan Peng Souvik Kundu Pierluigi Nuzzo