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.

通过对图结构进行连续松弛和参数化，我们提出了一种名为 Differentiable Graph Attack（DGA）的新型攻击方法，以高效生成有效的攻击并同时消除了昂贵的重新训练的需求。与最新技术相比，DGA 在不同基准数据集上实现了几乎相等的攻击性能，训练时间减少了 6 倍，GPU 内存占用减少了 11 倍。此外，我们还对 DGA 在不同图模型之间的可转移性以及对广泛使用的防御机制的鲁棒性进行了广泛的实验分析。