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State of the art on adversarial attacks and defenses in graphs

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Abstract

Graph neural networks (GNNs) had shown excellent performance in complex graph data modelings such as node classification, link prediction and graph classification. However, GNNs are vulnerable to adversarial attacks resulting in severe performance degradation, which brings many security and privacy issues. Such vulnerability of GNNs limits its application in safety–critical fields such as finance and transportation. Studying the principles and implementation of graph adversarial attacks and their countermeasures can provide insight into the reason behind the vulnerability of GNNs and consequently improve the robustness and generalization of the model. This paper introduces the related concepts of existing graph adversarial attack and defense algorithms and analyzes the basic idea and implementation of each algorithm. Moreover, we compare the strategies, target tasks, advantages and disadvantages of typical algorithms. Through the summary of the state of the art, the limitations and possible development directions of graph adversarial attacks and defenses are analyzed, which provides a useful reference for the further develo** relative researches.

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Acknowledgements

This work was supported by National Nature Science Foundation of China (No. 61502262).

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  1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China

    Zhengli Zhai, Penghui Li & Shu Feng

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  1. Zhengli Zhai
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Zhai, Z., Li, P. & Feng, S. State of the art on adversarial attacks and defenses in graphs. Neural Comput & Applic 35, 18851–18872 (2023). https://doi.org/10.1007/s00521-023-08839-9

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