Graph Neural Networks (GNNs) have achieved promising performance in various
real-world applications. However, recent studies have shown that GNNs are
vulnerable to adversarial attacks. In this paper, we study a
recently-introduced realistic attack scenario on graphs -- graph injection
attack (GIA). In the GIA scenario, the adversary is not able to modify the
existing link structure and node attributes of the input graph, instead the
attack is performed by injecting adversarial nodes into it. We present an
analysis on the topological vulnerability of GNNs under GIA setting, based on
which we propose the Topological Defective Graph Injection Attack (TDGIA) for
effective injection attacks. TDGIA first introduces the topological defective
edge selection strategy to choose the original nodes for connecting with the
injected ones. It then designs the smooth feature optimization objective to
generate the features for the injected nodes. Extensive experiments on
large-scale datasets show that TDGIA can consistently and significantly
outperform various attack baselines in attacking dozens of defense GNN models.
Notably, the performance drop on target GNNs resultant from TDGIA is more than
double the damage brought by the best attack solution among hundreds of
submissions on KDD-CUP 2020.

本文研究了图神经网络受到注入攻击威胁的拓扑漏洞，在此基础上提出了一种效果显著的注入攻击方法 ——TDGIA，通过拓扑缺陷边选择策略和平滑特征优化目标生成虚假节点特征，对防御 GNN 模型进行攻击并成功超过多种攻击基准。