Graph Adversarial Attack via Rewiring
Graph Adversarial Attack via Rewiring
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DOI：
10.1145/3447548.3467416
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Yao Ma;Suhang Wang;Tyler Derr;Lingfei Wu;Jiliang Tang
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文献类型：
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作者：
Yao Ma;Suhang Wang;Tyler Derr;Lingfei Wu;Jiliang Tang
图神经网络(GNN)在学习图结构数据表示方面表现出了强大的能力。因此，它们提高了许多与图相关的任务的性能，例如节点分类和图分类。然而，最近的研究表明，GNN很容易受到对抗性攻击。故意将精心创建的不可察觉的扰动添加到图形中，可能会在很大程度上损害它们的性能。现有的攻击方法经常通过添加/删除几条边来产生扰动，即使当修改的边的数量很小时，这也可能是明显的。在本文中，我们提出了一种图重新布线操作来执行攻击。与添加/删除边等现有操作相比，它会以一种不太明显的方式影响图形。然后，我们利用深度强化学习来学习策略，从而有效地执行重新布线操作。在真实图形上的实验证明了该框架的有效性。为了理解所提出的框架，我们进一步分析了其产生的扰动如何影响目标模型以及重新布线操作的优势。建议的框架的实施可在https://github.com/alge24/ReWatt.上查看
Graph Neural Networks (GNNs) have demonstrated their powerful capability in learning representations for graph-structured data. Consequently, they have enhanced the performance of many graph-related tasks such as node classification and graph classification. However, it is evident from recent studies that GNNs are vulnerable to adversarial attacks. Their performance can be largely impaired by deliberately adding carefully created unnoticeable perturbations to the graph. Existing attacking methods often produce perturbation by adding/deleting a few edges, which might be noticeable even when the number of modified edges is small. In this paper, we propose a graph rewiring operation to perform the attack. It can affect the graph in a less noticeable way compared to existing operations such as adding/deleting edges. We then utilize deep reinforcement learning to learn the strategy to effectively perform the rewiring operations. Experiments on real-world graphs demonstrate the effectiveness of the proposed framework. To understand the proposed framework, we further analyze how its generated perturbation impacts the target model and the advantages of the rewiring operations. The implementation of the proposed framework is available at https://github.com/alge24/ReWatt.