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.

基于 Graph Lottery Tickets 和 adversarially robust graph sparsification 的研究，我们发现稀疏的邻接矩阵和稀疏的图神经网络能够显著减少推理延迟和计算占用空间。针对不同的结构扰动攻击，我们提出了 ARGS 框架，通过优化新的损失函数，对邻接矩阵和网络权重进行裁剪，以提高鲁棒性。我们的评估结果表明，ARGS 生成的 Graph Lottery Tickets 能够在不同的训练时间结构攻击下显著提高鲁棒性。