Link Prediction Adversarial Attack

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 real-world data sets, we can conclude that most deep model based and other state-of-art link prediction algorithms cannot escape the adversarial attack just like GAE. We can benefit the attack as an efficient privacy protection tool from link prediction unknown violation, on the other hand, link prediction attack can be a robustness evaluation metric for current link prediction algorithm in attack defensibility.

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Cited by in corpus (10)

• Adversarial Attack and Defense on Graph Data: A Survey
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
• A Survey of Adversarial Learning on Graphs
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
• Adversarial Attack on Hierarchical Graph Pooling Neural Networks
• Can Adversarial Network Attack be Defended?
• GraphAttacker: A General Multi-Task GraphAttack Framework
• N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
• Auditing the Sensitivity of Graph-based Ranking with Visual Analytics
• A Game-Theoretic Algorithm for Link Prediction