Title: Adversarial Attack on Graph Structured Data,
Abstract: Deep learning on graph structures has shown exciting results in various
applications. However, few attentions have been paid to the robustness of such
models, in contrast to numerous research work for image or text adversarial
attack and defense. In this paper, we focus on the adversarial attacks that
fool the model by modifying the combinatorial structure of data. We first
propose a reinforcement learning based attack method that learns the
generalizable attack policy, while only requiring prediction labels from the
target classifier. Also, variants of genetic algorithms and gradient methods
are presented in the scenario where prediction confidence or gradients are
available. We use both synthetic and real-world data to show that, a family of
Graph Neural Network models are vulnerable to these attacks, in both
graph-level and node-level classification tasks. We also show such attacks can
be used to diagnose the learned classifiers.