  Graph Neural Networks (GNNs) have been successfully exploited in graph
analysis tasks in many real-world applications. However, GNNs have been shown
to have potential security issues imposed by adversarial samples generated by
attackers, which achieved great attack performance with almost imperceptible
perturbations. What limit the wide application of these attackers are their
methods' specificity on a certain graph analysis task, such as node
classification or link prediction. We thus propose GraphAttacker, a novel
generic graph attack framework that can flexibly adjust the structures and the
attack strategies according to the graph analysis tasks. Based on the
Generative Adversarial Network (GAN), GraphAttacker generates adversarial
samples through alternate training on three key components, the Multi-strategy
Attack Generator (MAG), the Similarity Discriminator (SD), and the Attack
Discriminator(AD). Furthermore, to achieve attackers within perturbation
budget, we propose a novel Similarity Modification Rate (SMR) to quantify the
similarity between nodes thus constrain the attack budget. We carry out
extensive experiments and the results show that GraphAttacker can achieve
state-of-the-art attack performance on graph analysis tasks of node
classification, graph classification, and link prediction. Besides, we also
analyze the unique characteristics of each task and their specific response in
the unified attack framework. We will release GraphAttacker as an open-source
simulation platform for future attack researches.
