With the rapid development of artificial intelligence, a series of machine learning algorithms, e.g., graph neural networks , have been proposed to facilitate network analysis or graph data mining. Unfortunately , recent studies indicate that such advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are purposely changed. However, little is known what's the difference between adversarial nodes and clean nodes, and what's the preference of each attack method, in terms of network structure. In this paper , we theoretically investigate three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax , and find that different attack methods have their specific attack preferences on changing network structure . Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method. Based on these findings, we further utilize the network attributes  to design machine learning models for adversarial sample detection and attack method recognition , achieving the outstanding performance.
With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks have been proposed to facilitate network analysis or graph data mining. Although effective , recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This paper investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax . It is found that different attack methods have their specific attack preferences on changing the target network structures . Such attack pattern are further verified by experimental results on some real-world networks, revealing that generally the top four most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.
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