### Review and comparative analysis of attack and defence algorithms on graph-based ANN architectures

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D.E. Namiot, E.A. Ilyshin, and I.V. Chizhov. “Attacks on Machine Learning Systems - Common Problems and Methods”. In: International Journal of Open Information Technologies 10.3 (2022), pp. 17–22.

Wei Jin et al. “Adversarial Attacks and Defenses on Graphs”. In: SIGKDD Explor. Newsl. (2021), pp. 19– 34.

Dan Hendrycks et al. “Unsolved problems in ml safety”. In: arXiv preprint arXiv:2109.13916 (2021).

D.E. Namiot, E.A. Ilyshin, and I.V. Chizhov. “On- going academic and industrial projects dedicated to robust machine learning”. In: International Journal of Open Information Technologies 9.10 (2021), pp. 35–46.

Daniel Zügner, Amir Akbarnejad, and Stephan Gün- nemann. “Adversarial attacks on neural networks for graph data”. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge dis- covery & data mining. 2018, pp. 2847–2856.

Shen Wang et al. “Adversarial defense frame- work for graph neural network”. In: arXiv preprint arXiv:1905.03679 (2019).

Lichao Sun et al. “Adversarial attack and defense on graph data: A survey”. In: IEEE Transactions on Knowledge and Data Engineering (2022).

Liang Chen et al. “A survey of adversarial learning on graphs”. In: arXiv preprint arXiv:2003.05730 (2020).

Hanjun Dai et al. “Adversarial attack on graph struc- tured data”. In: International conference on machine learning. PMLR. 2018, pp. 1115–1124.

Kaidi Xu et al. “Topology attack and defense for graph neural networks: An optimization perspective”. In: arXiv preprint arXiv:1906.04214 (2019).

Daniel Zügner and Stephan Günnemann. Adversarial Attacks on Graph Neural Networks via Meta Learning. 2019. arXiv: 1902.08412 [cs.LG].

Xiaoyun Wang et al. “Attack graph convolutional networks by adding fake nodes”. In: arXiv preprint arXiv:1810.10751 (2018).

Jinyin Chen et al. “Fast gradient attack on network em- bedding”. In: arXiv preprint arXiv:1809.02797 (2018).

Marcin Waniek et al. “Attack tolerance of link pre- diction algorithms: How to hide your relations in a social network”. In: arXiv preprint arXiv:1809.00152 (2018).

Jinyin Chen et al. “Link prediction adversarial attack”. In: arXiv preprint arXiv:1810.01110 (2018).

Jinyin Chen et al. “Time-aware gradient attack on dy- namic network link prediction”. In: IEEE Transactions on Knowledge and Data Engineering (2021).

Liang Chen et al. “Data poisoning attacks on neighborhood-based recommender systems”. In: Transactions on Emerging Telecommunications Technologies 32.6 (2021), e3872.

Huijun Wu et al. “Adversarial examples on graph data: Deep insights into attack and defense”. In: arXiv preprint arXiv:1903.01610 (2019).

Dingyuan Zhu et al. “Robust graph convolutional networks against adversarial attacks”. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019, pp. 1399– 1407.

Ke Sun et al. “Virtual adversarial training on graph convolutional networks in node classification”. In: Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV 2019, Xi’an, China, November 8–11, 2019, Proceedings, Part I 2. Springer. 2019, pp. 431–443.

Fuli Feng et al. “Graph adversarial training: Dynami- cally regularizing based on graph structure”. In: IEEE Transactions on Knowledge and Data Engineering 33.6 (2019), pp. 2493–2504.

Jinyin Chen et al. “Can adversarial network attack be defended?” In: arXiv preprint arXiv:1903.05994 (2019).

Daniel Zügner and Stephan Günnemann. “Certifiable robustness and robust training for graph convolutional networks”. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, pp. 246–256.

Felix Mujkanovic et al. “Are Defenses for Graph Neural Networks Robust?” In: Advances in Neural Information Processing Systems 35 (2022), pp. 8954–8968.

Stephan Günnemann. “Graph neural networks: Adver- sarial robustness”. In: Graph Neural Networks: Foun- dations, Frontiers, and Applications (2022), pp. 149– 176.

Wei Jin et al. “Graph structure learning for robust graph neural networks”. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2020, pp. 66–74.

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