Graph Neural Networks (GNNs) have drawn significant attentions over the years
and been broadly applied to vital fields that require high security standard
such as product recommendation and traffic forecasting. Under such scenarios,
exploiting GNN's vulnerabilities and further downgrade its classification
performance become highly incentive for adversaries. Previous attackers mainly
focus on structural perturbations of existing graphs. Although they deliver
promising results, the actual implementation needs capability of manipulating
the graph connectivity, which is impractical in some circumstances. In this
work, we study the possibility of injecting nodes to evade the victim GNN
model, and unlike previous related works with white-box setting, we
significantly restrict the amount of accessible knowledge and explore the
black-box setting. Specifically, we model the node injection attack as a Markov
decision process and propose GA2C, a graph reinforcement learning framework in
the fashion of advantage actor critic, to generate realistic features for
injected nodes and seamlessly merge them into the original graph following the
same topology characteristics. Through our extensive experiments on multiple
acknowledged benchmark datasets, we demonstrate the superior performance of our
proposed GA2C over existing state-of-the-art methods. The data and source code
are publicly accessible at: this https URL.

本文通过建立节点注入攻击的马尔科夫决策过程模型，提出了一种基于强化学习框架 （GA2C）的节点注入方法，将注入节点结构化地插入原图中。研究结果表明，与现有最先进的方法相比，本文提出的 GA2C 方法具有更高的注入成功率和分类误差率。