图神经网络对抗攻击与鲁棒性评测前沿进展OA北大核心CSTPCD

In recent years,graph neural networks(GNNs)have gradually become an important research direction in artificial intelligence.However,the adversarial vulnerability of GNNs poses severe challenges to their practical ap-plications.To gain a comprehensive understanding of adversarial attacks and robustness evaluation on GNNs,related state-of-the-art advancements are reviewed and discussed.Firstly,this paper introduces the research background of adversarial attacks on GNNs,provides a formal definition of these attacks,and elucidates the basic concepts and framework for research on adversarial attacks and robustness evaluation in GNNs.Following this,this paper gives an overview of the specific methods proposed in the field of adversarial attacks on GNNs,and details the foremost methods while categorizing them based on the type of adversarial attack and range of attack targets.Their operating mechanisms,principles,and pros and cons are also analyzed.Additionally,considering the model robustness evalua-tion's dependency on adversarial attack methods and adversarial perturbation degree,this paper focuses on direct evaluation indicators.To aid in designing and evaluating adversarial attack methods and GNNs'robust models,this paper compares representative methods considering implementation ease,accuracy,and execution time.This paper foresees ongoing challenges and future research areas.Current research on GNNs'adversarial robustness is experiment-oriented,lacking a guiding theoretical framework,necessitating further systematic theoretical research to ensure GNN-based systems'trustworthiness.