  Graph neural networks (GNNs) have been successfully exploited in graph
analysis tasks in many real-world applications. The competition between attack
and defense methods also enhances the robustness of GNNs. In this competition,
the development of adversarial training methods put forward higher requirement
for the diversity of attack examples. By contrast, most attack methods with
specific attack strategies are difficult to satisfy such a requirement. To
address this problem, we propose GraphAttacker, a novel generic graph attack
framework that can flexibly adjust the structures and the attack strategies
according to the graph analysis tasks. GraphAttacker generates adversarial
examples through alternate training on three key components: the multi-strategy
attack generator (MAG), the similarity discriminator (SD), and the attack
discriminator (AD), based on the generative adversarial network (GAN).
Furthermore, we introduce a novel similarity modification rate SMR to conduct a
stealthier attack considering the change of node similarity distribution.
Experiments on various benchmark datasets demonstrate that GraphAttacker can
achieve state-of-the-art attack performance on graph analysis tasks of node
classification, graph classification, and link prediction, no matter the
adversarial training is conducted or not. Moreover, we also analyze the unique
characteristics of each task and their specific response in the unified attack
framework. The project code is available at
https://github.com/honoluluuuu/GraphAttacker.
