[38] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations, 2017.
[39] Amy Lauger, Billy Wisniewski, and Laura McKenna. Disclosure avoidance techniques at the us census bureau: Current practices and research. Center for Disclosure Avoidance Research, US Census Bureau, 2014.
[40] Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, and Zoubin Ghahrami. Kronecker graphs: An approach to modeling networks. Journal of Machine Learning Research, 11(2), 2010.
[41] Yang Li, Michael Purcell, Thierry Rakotoarivelo, David Smith, Thilina Ranbaduge, and Kee Siong Ng. Private graph data release: A survey. ACM Computing Surveys, 55(11): 1-39, 2023.
[42] Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. Gated graph sequence neural networks. International Conference on Learning Representations, 2016.
[43] Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S. Zemel. Efficient graph generation with graph recurrent attention networks. Advances in Neural Information Processing Systems, 32, 2019.
[44] Gabriel Loaiza-Ganem and John P Cunningham. The continuous bernoulli: fixing a pervasive error in variational autoencoders. Advances in Neural Information Processing Systems, 32, 2019.
[45] Tengfei Ma, Jie Chen, and Cao Xiao. Constrained generation of semantically valid graphs via regularizing variational autoencoders. Advances in Neural Information Processing Systems, 31, 2018.
[46] Emanuele Martorana, Giovanni Micale, Alfredo Ferro, and Alfredo Pulvirenti. Establish the expected number of induced motifs on unlabeled graphs through analytical models. Applied Network Science, 5(1):1-23, 2020.
[47] Nikhil Mehta, Lawrence Carin, and Piyush Rai. Stochastic blockmodels meet graph neural networks. International Conference on Machine Learning, 97:4466-4474, 2019.
[48] Darakhshan Mir and Rebecca N Wright. A differentially private estimator for the stochastic kronecker graph model. The 2012 Joint EDBT/ICDT Workshops, pages 167-176, 2012.
[49] Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. AAAI Conference on Artificial Intelligence, pages 2901-2907, 2015.
[50] Arvind Narayanan and Vitaly Shmatikov. Robust de-anonymization of large sparse datasets. Symposium on Security and Privacy, pages 111-125, 2008.
[51] Arvind Narayanan and Vitaly Shmatikov. De-anonymizing social networks. IEEE Symposium on Security and Privacy, pages 173-187, 2009.
[52] Mark Newman. Networks. Oxford university press, 2018.
[53] Giannis Nikolentzos, Giannis Siglidis, and Michalis Vazirgiannis. Graph kernels: A survey. Journal of Artificial Intelligence Research, 72:943-1027, 2021.
[54] Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. Permutation invariant graph generation via score-based generative modeling. International Conference on Artificial Intelligence and Statistics, 108:4474-4484, 2020.
[55] Leslie O'Bray, Max Horn, Bastian Rieck, and Karsten Borgwardt. Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions. International Conference on Learning Representations, 2022.