[31] T. T. Mueller, J. C. Paetzold, C. Prabhakar, D. Usynin, D. Ruekert, and G. Kaissis, "Differentially private graph neural networks for whole-graph classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[32] I. E. Olatunji, T. Funke, and M. Khosla, "Releasing graph neural networks with differential privacy guarantees," arXiv preprint arXiv:2109.08907, 2021.
[33] N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar, "Semi-supervised knowledge transfer for deep learning from private training data," arXiv preprint arXiv:1610.05755, 2016.
[34] S. Asoodeh, J. Liao, F. P. Calmon, O. Kosut, and L. Sankar, “A better bound gives a hundred rounds: Enhanced privacy guarantees via f-divergences,” in 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020, pp. 920–925.
[35] C. L. Canonne, G. Kamath, and T. Steinke, “The discrete gaussian for differential privacy,” arXiv preprint arXiv:2004.00010, 2020.
[36] M. Bun and T. Steinke, “Concentrated differential privacy: Simplifications, extensions, and lower bounds,” in Theory of Cryptography Conference. Springer, 2016, pp. 635–658.
[37] E. Chien, C. Pan, and O. Milenkovic, "Efficient model updates for approximate unlearning of graph-structured data," in The Eleventh International Conference on Learning Representations, 2023. [Online]. Available: https://openreview.net/forum?id=fhcu4FBLciL
[38] C. Pan, E. Chien, and O. Milenkovic, "Unlearning graph classifiers with limited data resources," in Proceedings of the ACM Web Conference 2023, ser. WWW '23. New York, NY, USA: Association for Computing Machinery, 2023, p. 716-726. [Online]. Available: https://doi.org/10.1145/3543507.3583547
[39] D. Lim, F. Hohne, X. Li, S. L. Huang, V. Gupta, O. Bhalerao, and S. N. Lim, "Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods," Advances in Neural Information Processing Systems, vol. 34, pp. 20887-20902, 2021.
[40] I. Mironov, “Rényi differential privacy,” in 2017 IEEE 30th Computer Security Foundations Symposium (CSF). IEEE, 2017, pp. 263–275.
[41] C. Dwork, A. Roth et al., “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science, vol. 9, no. 3–4, pp. 211–407, 2014.
[42] M. Fey and J. E. Lenssen, "Fast graph representation learning with pytorch geometric," arXiv preprint arXiv:1903.02428, 2019.
[43] A. L. Traud, P. J. Mucha, and M. A. Porter, "Social structure of facebook networks," Physica A: Statistical Mechanics and its Applications, vol. 391, no. 16, pp. 4165-4180, 2012.
[44] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, "Collective classification in network data," AI magazine, vol. 29, no. 3, pp. 93-93, 2008.
[45] Z. Yang, W. Cohen, and R. Salakhudinov, "Revisiting semi-supervised learning with graph embeddings," in International conference on machine learning. PMLR, 2016, pp. 40-48.
[46] O. Shchur, M. Mumme, A. Bojchevski, and S. Gunnemann, “Pitfalls of graph neural network evaluation,” arXiv preprint arXiv:1811.05868, 2018.
[47] B. Rozemberczki, C. Allen, and R. Sarkar, “Multi-scale attributed node embedding,” Journal of Complex Networks, vol. 9, no. 2, p. cnab014, 2021.
[48] C. Sun and G. Wu, "Scalable and adaptive graph neural networks with self-label-enhanced training," arXiv preprint arXiv:2104.09376, 2021.
[49] W. Zhang, Z. Yin, Z. Sheng, Y. Li, W. Ouyang, X. Li, Y. Tao, Z. Yang, and B. Cui, “Graph attention multi-layer perceptron,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 4560–4570.
[50] P. Kairouz, S. Oh, and P. Viswanath, “The composition theorem for differential privacy,” in International conference on machine learning. PMLR, 2015, pp. 1376–1385.
[51] T. Van Erven and P. Harremos, “Rényi divergence and kullback-leibler divergence,” IEEE Transactions on Information Theory, vol. 60, no. 7, pp. 3797–3820, 2014.