Adversarial Attack on Large Scale Graph

Recent studies have shown that graph neural networks are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Most works on attacking the graph neural networks are currently mainly using the gradient information to guide the attack and achieve outstanding performance. Nevertheless, the high complexity of time and space makes them unmanageable for large scale graphs. We argue that the main reason is that they have to use the entire graph for attacks, resulting in the increasing time and space complexity as the data scale grows. In this work, we propose an efficient Simplified Gradient-based Attack (SGA) framework to bridge this gap. SGA can cause the graph neural networks to misclassify specific target nodes through a multi-stage optimized attack framework, which needs only a much smaller subgraph. In addition, we present a practical metric named Degree Assortativity Change (DAC) for measuring the impacts of adversarial attacks on graph data. We evaluate our attack method on four real-world datasets by attacking several commonly used graph neural networks. The experimental results show that SGA is able to achieve significant time and memory efficiency improvements while maintaining considerable performance in the attack compared to other state-of-the-art methods of attack.

Data Poisoning Attacks on Neighborhood-based Recommender Systems

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.