  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.
