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Real-world graph applications, such as advertisements and productrecommendations make profits based on accurately classify the label of thenodes. However, in such scenarios, there are high incentives for theadversaries to attack such graph to reduce the node classification performance.Previous work on graph adversarial attacks focus on modifying existing graphstructures, which is infeasible in most real-world applications. In contrast,it is more practical to inject adversarial nodes into existing graphs, whichcan also potentially reduce the performance of the classifier. In this paper,we study the novel node injection poisoning attacks problem which aims topoison the graph. We describe a reinforcement learning based method, namelyNIPA, to sequentially modify the adversarial information of the injected nodes.We report the results of experiments using several benchmark data sets thatshow the superior performance of the proposed method NIPA, relative to theexisting state-of-the-art methods.

Node Injection Attacks on Graphs via Reinforcement Learning