Abstract

Recent studies have demonstrated that machine learning approaches like deep learning methods are easily fooled by adversarial attacks. Recently, a highly-influential study examined the impact of adversarial attacks on graph data and demonstrated that graph embedding techniques are also vulnerable to adversarial attacks. Fake users on social media and fake product reviews are examples of perturbations in graph data that are realistic counterparts of the adversarial models proposed. Graphs are widely used in a variety of domains and it is highly important to develop graph analysis techniques that are robust to adversarial attacks. One of the recent studies on generating adversarial attacks for graph data is Nettack. The Nettack model has shown to be very successful in deceiving the Graph Convolutional Network (GCN) model. Nettack is also transferable to other node classification approaches e.g. node embeddings. In this paper, we explore the properties of Nettack perturbations, in search for effective defenses against them. Our first finding is that Nettack demonstrates a very specific behavior in the spectrum of the graph: only high-rank (low-valued) singular components of the graph are affected. Following that insight, we show that a low-rank approximation of the graph, that uses only the top singular components for its reconstruction, can greatly reduce the effects of Nettack and boost the performance of GCN when facing adversarial attacks. Indicatively, on the CiteSeer dataset, our proposed defense mechanism is able to reduce the success rate of Nettack from 98% to 36%. Furthermore, we show that tensor-based node embeddings, which by default project the graph into a low-rank subspace, are robust against Nettack perturbations. Lastly, we propose LowBlow, a low-rank adversarial attack which is able to affect the classification performance of both GCN and tensor-based node embeddings and we show that the low-rank attack is noticeable and making it unnoticeable results in a high-rank attack.