[48] Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M. Bronstein. Fake news detection on social media using geometric deep learning, 2019. URL https://arxiv.org/abs/1902.06673.
[49] Sebastian Moreno, Jennifer Neville, and Sergey Kirshner. Tied Kronecker product graph models to capture variance in network populations. ACM Transactions on Knowledge Discovery from Data, 12(3), 2018. doi: 10.1145/3161885.
[50] Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. Weisfeiler and Leman go neural: Higher-order graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1):4602-4609, 2019. doi: 10.1609/aaai.v33i01.33014602.
[51] Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. TUDataset: A collection of benchmark datasets for learning with graphs, 2020. URL https://arxiv.org/abs/2007.08663.
[52] Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Krieger, Martin Grohe, Matthias Fey, and Karsten Borgwardt. Weisfeiler and Leman go machine learning: The story so far, 2021. URL https://arxiv.org/abs/2112.09992.
[53] Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. Permutation invariant graph generation via score-based generative modeling, 2020. URL https://arxiv.org/abs/2003.00638.
[54] Leslie O'Bray, Max Horn, Bastian Rieck, and Karsten Borgwardt. Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions. In International Conference on Learning Representations (ICLR), 2022.
[55] Yann Ollivier. Ricci curvature of metric spaces. Comptes Rendus Mathematique, 345(11): 643-646, 2007. doi: 10.1016/j.crma.2007.10.041.
[56] Pál András Papp and Roger Wattenhofer. A theoretical comparison of graph neural network extensions, 2022. URL https://arxiv.org/abs/2201.12884.
[57] Bastian Rieck. On the expressivity of persistent homology in graph learning, 2023. URL https://arxiv.org/abs/2302.09826.
[58] Bastian Rieck, Filip Sadlo, and Heike Leitte. Topological machine learning with persistence indicator functions. In Hamish Carr, Issei Fujishiro, Filip Sadlo, and Shigeo Takahashi, editors, Topological Methods in Data Analysis and Visualization V, pages 87-101. Springer, Cham, Switzerland, 2020. doi: 10.1007/978-3-030-43036-8_6.
[59] Camilo Ruiz, Marinka Zitnik, and Jure Leskovec. Identification of disease treatment mechanisms through the multiscale interactome. Nature Communications, 12(1):1796, 2021.
[60] Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, and Debarghya Ghoshdastidar. Graphon based clustering and testing of networks: Algorithms and theory. In International Conference on Learning Representations (ICLR), 2022. URL https://openreview.net/forum?id=sTNHCrIKDQc.
[61] Areejit Samal, R. P. Sreejith, Jiao Gu, Shiping Liu, Emil Saucan, and Jürgen Jost. Comparative analysis of two discretizations of Ricci curvature for complex networks. Scientific Reports, 8 (1), 2018. doi: 10.1038/s41598-018-27001-3.
[62] Emil Saucan, Areejit Samal, and Jürgen Jost. A simple differential geometry for complex networks. Network Science, 9(S1):S106-S133, 2021. doi: 10.1017/nws.2020.42.
[63] Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, and Karsten Borgwardt. Wasserstein Weisfeiler-Lehman graph kernels. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems (NeurIPS), volume 32. Curran Associates, Inc., 2019.