Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria

Adversarial attacks have been alerting the artificial intelligence community recently, since many machine learning algorithms were found vulnerable to malicious attacks. This paper studies adversarial attacks to scale-free networks to test their robustness in terms of statistical measures. In addition to the well-known random link rewiring (RLR) attack, two heuristic attacks are formulated and simulated: degree-addition-based link rewiring (DALR) and degree-interval-based link rewiring (DILR). These three strategies are applied to attack a number of strong scale-free networks of various sizes generated from the Barabási-Albert model. It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack. However, DILR is as concealed as RLR in the sense that they both are constructed by introducing a relatively small number of changes on several typical structural properties such as average shortest path-length, average clustering coefficient, and average diagonal distance. The results of this paper suggest that to classify a network to be scale-free has to be very careful from the viewpoint of adversarial attack effects.