[34] Chuntao Jiang, Frans Coenen, and Michele Zito. Finding frequent subgraphs in longitudinal social network data using a weighted graph mining approach. In Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I 6, pages 405-416. Springer, 2010.
[35] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Junction tree variational autoencoder for molecular graph generation. In International conference on machine learning, pages 2323-2332. PMLR, 2018.
[36] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[37] Mehmet Koyutürk, Ananth Grama, and Wojciech Szpankowski. An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics, 20(suppl_1):i200-i207, 2004.
[38] Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34:21618-21629, 2021.
[39] Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. Distance encoding: Design provably more powerful neural networks for graph representation learning. Advances in Neural Information Processing Systems, 33:4465-4478, 2020.
[40] Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji. Spherical message passing for 3d graph networks. arXiv preprint arXiv:2102.05013, 2021.
[41] Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902, 2018.
[42] Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. Provably powerful graph networks. Advances in neural information processing systems, 32, 2019.
[43] Haggai Maron, Ethan Fetaya, Nimrod Segol, and Yaron Lipman. On the universality of invariant networks. In International conference on machine learning, pages 4363-4371. PMLR, 2019.
[44] 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. In Proceedings of the AAAI conference on artificial intelligence, pages 4602-4609, 2019.
[45] Christopher Morris, Gaurav Rattan, and Petra Mutzel. Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings. Advances in Neural Information Processing Systems, 33:21824-21840, 2020.
[46] Chendi Qian, Gaurav Rattan, Floris Geerts, Mathias Niepert, and Christopher Morris. Ordered subgraph aggregation networks. In Advances in Neural Information Processing Systems, 2022.
[47] Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R Manby, and Thomas F Miller III. Orbnet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features. The Journal of chemical physics, 153(12):124111, 2020.
[48] Behrooz Tahmasebi, Derek Lim, and Stefanie Jegelka. Counting substructures with higher-order graph neural networks: Possibility and impossibility results. arXiv preprint arXiv:2012.03174, 2020.
[49] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[50] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
[51] Yanbo Wang and Muhan Zhang. Towards better evaluation of gnn expressiveness with brec dataset. arXiv preprint arXiv:2304.07702, 2023.