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

@inproceedings{iclr2020_the_29762,
  title = {{The Logical Expressiveness of Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=r1lZ7AEKvB},
}

@inproceedings{aaai2020_graph_101402,
  title = {{Graph Representations for Higher-Order Logic and Theorem Proving}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5689},
}

@inproceedings{iclr2021_analyzing_29937,
  title = {{Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3158},
}

@inproceedings{icml2021_directional_39250,
  title = {{Directional Graph Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10053},
}

@inproceedings{iclr2021_expressive_30605,
  title = {{Expressive Power of Invariant and Equivariant Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2559},
}

@inproceedings{neurips2022_understanding_66182,
  title = {{Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54892},
}

@inproceedings{iclr2023_rethinking_33235,
  title = {{Rethinking the Expressive Power of GNNs via Graph Biconnectivity}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12158},
}

@inproceedings{neurips2022_provably_64730,
  title = {{Provably expressive temporal graph networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53434},
}

@inproceedings{icml2021_gnnautoscale_39371,
  title = {{GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10757},
}

@inproceedings{iclr2021_on_30697,
  title = {{On Graph Neural Networks versus Graph-Augmented MLPs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3074},
}

@inproceedings{iclr2022_expressiveness_31819,
  title = {{Expressiveness and Approximation Properties of Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6804},
}

@inproceedings{neurips2023_what_67818,
  title = {{What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71941},
}

@inproceedings{icml2022_a_40185,
  title = {{A Theoretical Comparison of Graph Neural Network Extensions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16097},
}

@inproceedings{neurips2021_reconstruction_62529,
  title = {{Reconstruction for Powerful Graph Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27462},
}

@inproceedings{neurips2024_logical_72881,
  title = {{Logical characterizations of recurrent graph neural networks with reals and floats}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94525},
}

@inproceedings{iclr2024_rethinking_35216,
  title = {{Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17857},
}

@inproceedings{icml2024_expressivity_45499,
  title = {{Expressivity and Generalization: Fragment-Biases for Molecular GNNs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32952},
}

@inproceedings{iclr2024_beyond_34081,
  title = {{Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18994},
}

@inproceedings{iclr2024_efficient_34945,
  title = {{Efficient Subgraph GNNs by Learning Effective Selection Policies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18129},
}

@inproceedings{icml2023_a_41480,
  title = {{A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25206},
}

@inproceedings{icml2024_an_44572,
  title = {{An Empirical Study of Realized GNN Expressiveness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33880},
}

@inproceedings{icml2024_on_45213,
  title = {{On the Expressive Power of Spectral Invariant Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33239},
}

@inproceedings{neurips2024_towards_71677,
  title = {{Towards Principled Graph Transformers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95605},
}

@inproceedings{iclr2025_learning_81142,
  title = {{Learning Efficient Positional Encodings with Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30628},
}

@inproceedings{iclr2025_kaa_82657,
  title = {{KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29141},
}

@inproceedings{iclr2025_what_83671,
  title = {{What Are Good Positional Encodings for Directed Graphs?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28146},
}

@inproceedings{aaai2026_enhancing_107663,
  title = {{Enhancing Logical Expressiveness in Graph Neural Networks via Path-Neighbor Aggregation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40015},
}

@inproceedings{aaai2025_expressive_9092,
  title = {{Expressive Power of Temporal Message Passing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35396},
}

@inproceedings{aaai2025_learning_9739,
  title = {{Learning More Expressive General Policies for Classical Planning Domains}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34872},
}

@inproceedings{iclr2024_g_34857,
  title = {{G\$^2\$N\$^2\$ : Weisfeiler and Lehman go grammatical}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18217},
}

@inproceedings{icml2024_aligning_43424,
  title = {{Aligning Transformers with Weisfeiler-Leman}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35028},
}

@inproceedings{icml2024_comparing_45675,
  title = {{Comparing Graph Transformers via Positional Encodings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32777},
}

@inproceedings{icml2025_weisfeiler_46228,
  title = {{Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46332},
}

@inproceedings{icml2026_on_53064,
  title = {{On the Expressive Power of GNNs to Solve Linear SDPs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=bjvLKXsTMK},
}

@inproceedings{icml2026_full_53887,
  title = {{Full-Spectrum Graph Neural Networks: Expressive and Scalable}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=jk7P5ppKxD},
}