[1] Edo M Airoldi, David Blei, Stephen Fienberg, and Eric Xing. Mixed membership stochastic blockmodels. Advances in Neural Information Processing Systems, 21, 2008.
[2] Héber H Arcolezi, Jean-François Couchot, Bechara Al Bouna, and Xiaokui Xiao. Random sampling plus fake data: Multidimensional frequency estimates with local differential privacy. International Conference on Information & Knowledge Management, pages 47-57, 2021.
[3] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer Normalization. stat, 1050:21, 2016.
[4] Nicola De Cao and Thomas Kipf. MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973, 2018.
[5] Fan Chung and Linyuan Lu. The average distances in random graphs with given expected degrees. The National Academy of Sciences, 99(25):15879-15882, 2002.
[6] Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, and Dale Schuurmans. Scalable deep generative modeling for sparse graphs. International Conference on Machine Learning, pages 2302-2312, 2020.
[7] Wei-Yen Day, Ninghui Li, and Min Lyu. Publishing graph degree distribution with node differential privacy. International Conference on Management of Data, pages 123-138, 2016.
[8] Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. Journal of medicinal chemistry, 34(2):786-797, 1991.
[9] Fred W DePiero and John K Carlin. Structural matching via optimal basis graphs. International Conference on Pattern Recognition, 3:449-452, 2006.
[10] Reinhard Diestel. Graph theory, 4th edition, volume 173 of Graduate texts in mathematics. Springer, 2012. ISBN 978-3-642-14278-9.
[11] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately. Advances in Neural Information Processing Systems, 30, 2017.
[12] Paul D Dobson and Andrew J Doig. Distinguishing enzyme structures from non-enzymes without alignments. Journal of Molecular Biology, 330(4):771-783, 2003.
[13] Cynthia Dwork. Differential privacy. International Colloquium on Automata, Languages, and Programming, pages 1-12, 2006.
[14] Cynthia Dwork and Jing Lei. Differential privacy and robust statistics. ACM Symposium on Theory of Computing, pages 371-380, 2009.
[15] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. Theory of Cryptography: Third Theory of Cryptography Conference, pages 265-284, 2006.
[16] Talya Eden, Amit Levi, Dana Ron, and C Seshadhri. Approximately counting triangles in sublinear time. SIAM Journal on Computing, 46(5):1603-1646, 2017.
[17] Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, and Peilin Zhong. Differentially private graph learning via sensitivity-bounded personalized pagerank. arXiv preprint arXiv:2207.06944, 2022.
[18] Paul Erdős, Alfréd Rényi, et al. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci, 5(1):17-60, 1960.
[19] Ülfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. Rappor: randomized aggregatable privacy-preserving ordinal response. The ACM SIGSAC Conference on Computer and Communications Security, pages 1054–1067, 2014.