% Key papers for Substructure-aware GNN expressivity (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2019_geniepath_101647,
  title = {{GeniePath: Graph Neural Networks with Adaptive Receptive Paths}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4354},
}

@inproceedings{aaai2023_substructure_4746,
  title = {{Substructure Aware Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26318},
}

@inproceedings{icml2021_weisfeiler_40111,
  title = {{Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10197},
}

@inproceedings{neurips2020_weisfeiler_61092,
  title = {{Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17474},
}

@inproceedings{icml2019_relational_37694,
  title = {{Relational Pooling for Graph Representations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4298},
}

@inproceedings{neurips2019_on_58741,
  title = {{On the equivalence between graph isomorphism testing and function approximation with GNNs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14474},
}

@inproceedings{neurips2020_can_59506,
  title = {{Can Graph Neural Networks Count Substructures?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17845},
}

@inproceedings{icml2023_on_41618,
  title = {{On the Expressive Power of Geometric Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24433},
}

@inproceedings{aaai2018_towards_101472,
  title = {{Towards a Unified Framework for Syntactic Inconsistency Measures}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11556},
}

@inproceedings{aaai2022_graph_2166,
  title = {{Graph Filtration Kernels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08196-graph-filtration-kernels/},
}

@inproceedings{iclr2022_equivariant_31492,
  title = {{Equivariant Subgraph Aggregation Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6212},
}

@inproceedings{neurips2023_a_69098,
  title = {{A new perspective on building efficient and expressive 3D equivariant graph neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70780},
}

@inproceedings{icml2022_equivariant_40510,
  title = {{Equivariant Quantum Graph Circuits}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16081},
}

@inproceedings{icml2022_going_40634,
  title = {{Going Deeper into Permutation-Sensitive Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16659},
}

@inproceedings{icml2020_graph_38264,
  title = {{Graph Homomorphism Convolution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6747},
}

@inproceedings{neurips2024_non_71536,
  title = {{Non-convolutional graph neural networks.}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95728},
}

@inproceedings{neurips2023_expressive_68298,
  title = {{Expressive Sign Equivariant Networks for Spectral Geometric Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71505},
}

@inproceedings{iclr2022_topological_31689,
  title = {{Topological Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6389},
}

@inproceedings{icml2022_convergence_40379,
  title = {{Convergence of Invariant Graph Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16105},
}

@inproceedings{icml2023_from_42043,
  title = {{From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23571},
}

@inproceedings{icml2024_homomorphism_45822,
  title = {{Homomorphism Counts for Graph Neural Networks: All About That Basis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32630},
}

@inproceedings{icml2024_weisfeiler_43990,
  title = {{Weisfeiler-Leman at the margin: When more expressivity matters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34462},
}

@inproceedings{icml2025_the_47474,
  title = {{The Generalized Skew Spectrum of Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45119},
}

@inproceedings{neurips2024_deep_71612,
  title = {{Deep Homomorphism Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95659},
}

@inproceedings{neurips2025_on_77559,
  title = {{On topological descriptors for graph products}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=VCORb5Fw8e},
}

@inproceedings{neurips2025_graph_80131,
  title = {{Graph Persistence goes Spectral}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=wU8IKGLpbi},
}

@inproceedings{iclr2025_generalizing_81556,
  title = {{Generalizing Weisfeiler-Lehman Kernels to Subgraphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30220},
}

@inproceedings{iclr2025_on_81914,
  title = {{On the Expressive Power of Sparse Geometric MPNNs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29870},
}

@inproceedings{iclr2025_on_82010,
  title = {{On the Hölder Stability of Multiset and Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29775},
}

@inproceedings{iclr2025_revisiting_82173,
  title = {{Revisiting Random Walks for Learning on Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29618},
}

@inproceedings{iclr2025_homomorphism_83634,
  title = {{Homomorphism Expressivity of Spectral Invariant Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28183},
}

@inproceedings{neurips2026_expressive_90552,
  title = {{Expressive Power of Deep Homomorphism Networks over Relational Databases}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152909},
}

@inproceedings{neurips2026_weisfeiler_93578,
  title = {{Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150655},
}

@inproceedings{aaai2026_graph_106093,
  title = {{Graph Choosability via SAT: Beyond the Nullstellensatz}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38441},
}

@inproceedings{iclr2024_on_34018,
  title = {{On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19057},
}

@inproceedings{iclr2024_rethinking_35173,
  title = {{Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17900},
}

@inproceedings{icml2024_on_44695,
  title = {{On the Generalization of Equivariant Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33757},
}

@inproceedings{icml2025_primphormer_48079,
  title = {{Primphormer: Efficient Graph Transformers with Primal Representations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44528},
}

@inproceedings{icml2025_improved_48600,
  title = {{Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44021},
}