[34] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Junction tree variational autoencoder for molecular graph generation. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML), volume 80 of Proceedings of Machine Learning Research, pages 2323-2332. PMLR, 2018.
[35] Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi S. Jaakkola. Iterative refinement graph neural network for antibody sequence-structure co-design. In International Conference on Learning Representations (ICLR), 2022. URL https://openreview.net/forum?id=LI2bhrE_2A.
[36] Pakawut Jiradilok and Supanat Kamtue. Transportation distance between probability measures on the infinite regular tree, 2021. URL https://arxiv.org/abs/2107.09876.
[37] Jürgen Jost and Shiping Liu. Ollivier's Ricci curvature, local clustering and curvature-dimension inequalities on graphs. Discrete & Computational Geometry, 51(2):300-322, 2014. doi: 10.1007/s00454-013-9558-1.
[38] John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romero-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis. Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873):583-589, 2021. doi: 10.1038/s41586-021-03819-2.
[39] Ana Justel, Daniel Pena, and Ruben Zamar. A multivariate Kolmogorov-Smirnov test of goodness of fit. Statistics & Probability Letters, 35(3):251-259, 1997. doi: 10.1016/S0167-7152(97)00020-5.
[40] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR), 2017. URL https://openreview.net/forum?id=SJU4ayYgl.
[41] Jack H. Koolen, Greg Markowsky, and Jongyook Park. On electric resistances for distance-regular graphs. European Journal of Combinatorics, 34(4):770–786, 2013. doi: 10.1016/j.ejc.2012.12.001.
[42] Michael Lingzhi Li, Meng Dong, Jiawei Zhou, and Alexander M. Rush. A hierarchy of graph neural networks based on learnable local features, 2019. URL https://arxiv.org/abs/1911.05256.
[43] Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. Distance encoding: Design provably more powerful neural networks for graph representation learning. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 4465-4478. Curran Associates, Inc., 2020.
[44] Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S. Zemel. Efficient graph generation with graph recurrent attention networks, 2019. URL https://arxiv.org/abs/1910.00760.
[45] Shiping Liu, Florentin Münch, and Norbert Peyerimhoff. Bakry-Émery curvature and diameter bounds on graphs. *Calculus of Variations and Partial Differential Equations*, 57(2), 2018. doi: 10.1007/s00526-018-1334-x.
[46] L. Lovász. Random walks on graphs: A survey. In D. Miklós, V. T. Sós, and T. Szonyi, editors, Combinatorics, Paul Erdős is Eighty, volume 2, pages 353-398. János Bolyai Mathematical Society, Budapest, 1996.
[47] Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, and Jeff Dean. A graph placement methodology for fast chip design. Nature, 594 (7862):207-212, 2021. doi: 10.1038/s41586-021-03544-w.