As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services. However, GNNs have been shown to be vulnerable to adversarial attacks, which can significantly degrade their effectiveness. Recent state-of-the-art approaches in adversarial attacks rely on gradient-based meta-learning to selectively perturb a single edge with the highest attack score until they reach the budget constraint. While effective in identifying vulnerable links, these methods are plagued by high computational costs. By leveraging continuous relaxation and parameterization of the graph structure, we propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks and meanwhile eliminate the need for costly retraining. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint on different benchmark datasets. Additionally, we provide extensive experimental analyses of the transferability of the DGA among different graph models, as well as its robustness against widely-used defense mechanisms.

翻译：作为图表示学习的有力工具，图神经网络（GNN）在社交网络、推荐系统和在线网络服务等应用中发挥了重要作用。然而，研究表明GNN易受对抗性攻击，这可能会显著降低其有效性。近期最先进的对抗攻击方法依赖基于梯度的元学习，通过选择性扰动具有最高攻击分数的单条边，直至达到预算约束。尽管这些方法能有效识别脆弱链接，但受限于高昂的计算成本。通过利用图结构的连续松弛和参数化，我们提出一种名为可微图攻击（DGA）的新型攻击方法，以高效生成有效攻击，同时消除昂贵重训练的需求。与最先进方法相比，DGA在不同基准数据集上实现了几乎等效的攻击性能，同时训练时间减少6倍，GPU内存占用减少11倍。此外，我们提供了广泛的实验分析，探讨DGA在不同图模型之间的可迁移性及其对广泛使用的防御机制的鲁棒性。