Title: Adversarial Attacks on Neural Networks for Graph Data,
Abstract: Deep learning models for graphs have achieved strong performance for the task
of node classification. Despite their proliferation, currently there is no
study of their robustness to adversarial attacks. Yet, in domains where they
are likely to be used, e.g. the web, adversaries are common. Can deep learning
models for graphs be easily fooled? In this work, we introduce the first study
of adversarial attacks on attributed graphs, specifically focusing on models
exploiting ideas of graph convolutions. In addition to attacks at test time, we
tackle the more challenging class of poisoning/causative attacks, which focus
on the training phase of a machine learning model. We generate adversarial
perturbations targeting the node's features and the graph structure, thus,
taking the dependencies between instances in account. Moreover, we ensure that
the perturbations remain unnoticeable by preserving important data
characteristics. To cope with the underlying discrete domain we propose an
efficient algorithm Nettack exploiting incremental computations. Our
experimental study shows that accuracy of node classification significantly
drops even when performing only few perturbations. Even more, our attacks are
transferable: the learned attacks generalize to other state-of-the-art node
classification models and unsupervised approaches, and likewise are successful
even when only limited knowledge about the graph is given.