Link Prediction Adversarial Attack
Jinyin Chen, Ziqiang Shi, Yangyang Wu, Xuanheng Xu, Haibin Zheng

TL;DR
This paper introduces the first formulation and attack method for link prediction adversarial attacks on graph auto-encoders, revealing their vulnerability and proposing potential uses in privacy protection and robustness evaluation.
Contribution
It defines the link prediction adversarial attack problem and proposes a novel iterative gradient attack method based on trained graph auto-encoders.
Findings
GAE is easily fooled with few link perturbations
Most deep and state-of-the-art link prediction algorithms are vulnerable
Adversarial attacks can be used for privacy protection and robustness assessment
Abstract
Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep model has also been revealed by carefully designed adversarial examples generated by various adversarial attack methods. With the wider application of deep model in complex network analysis, in this paper we define and formulate the link prediction adversarial attack problem and put forward a novel iterative gradient attack (IGA) based on the gradient information in trained graph auto-encoder (GAE). To our best knowledge, it is the first time link prediction adversarial attack problem is defined and attack method is brought up. Not surprisingly, GAE was easily fooled by adversarial network with only a few links perturbed on the clean network. By conducting comprehensive experiments on different…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Adversarial Robustness in Machine Learning · Complex Network Analysis Techniques
