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

@inproceedings{neurips2020_random_60665,
  title = {{Random Walk Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17331},
}

@inproceedings{cvpr2021_rethinking_17751,
  title = {{Rethinking Graph Neural Architecture Search From Message-Passing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2021_directed_30184,
  title = {{Directed Acyclic Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3236},
}

@inproceedings{aaai2023_graph_3797,
  title = {{Graph Ordering Attention Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25856},
}

@inproceedings{aaai2024_physics_6952,
  title = {{Physics-Informed Graph Neural Networks for Water Distribution Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30192},
}

@inproceedings{cvpr2023_from_21026,
  title = {{From Node Interaction To Hop Interaction: New Effective and Scalable Graph Learning Paradigm}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22058},
}

@inproceedings{aaai2025_mind_9952,
  title = {{Mind Individual Information! Principal Graph Learning for Multimedia Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33429},
}

@inproceedings{aaai2024_structural_7428,
  title = {{Structural Information Enhanced Graph Representation for Link Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29417},
}

@inproceedings{neurips2021_graph_62900,
  title = {{Graph Neural Networks with Adaptive Residual}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26726},
}

@inproceedings{aaai2023_learning_4039,
  title = {{Learning to Count Isomorphisms with Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25610},
}

@inproceedings{iclr2022_equivariant_31289,
  title = {{Equivariant Graph Mechanics Networks with Constraints}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6795},
}

@inproceedings{neurips2020_pointer_60589,
  title = {{Pointer Graph Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18082},
}

@inproceedings{neurips2021_reinforcement_63123,
  title = {{Reinforcement Learning Enhanced Explainer for Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28176},
}

@inproceedings{aaai2022_multi_2441,
  title = {{Multi-Scale Distillation from Multiple Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04337-multi-scale-distillation-from-multiple-graph-neural-networks/},
}

@inproceedings{iclr2023_anti_32369,
  title = {{Anti-Symmetric DGN: a stable architecture for Deep Graph Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11227},
}

@inproceedings{neurips2020_towards_60997,
  title = {{Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17656},
}

@inproceedings{neurips2021_vq_61698,
  title = {{VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26298},
}

@inproceedings{neurips2022_learning_66189,
  title = {{Learning Physical Dynamics with Subequivariant Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55060},
}

@inproceedings{cvpr2022_automatic_18323,
  title = {{Automatic Relation-Aware Graph Network Proliferation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2023_fosr_31991,
  title = {{FoSR: First-order spectral rewiring for addressing oversquashing in GNNs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11758},
}

@inproceedings{neurips2024_are_71734,
  title = {{Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95552},
}

@inproceedings{aaai2025_nlgt_10084,
  title = {{NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33413},
}

@inproceedings{neurips2024_on_73618,
  title = {{On the Scalability of GNNs for Molecular Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93869},
}

@inproceedings{iclr2024_polynormer_34988,
  title = {{Polynormer: Polynomial-Expressive Graph Transformer in Linear Time}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18086},
}

@inproceedings{aaai2024_a_5032,
  title = {{A Graph Dynamics Prior for Relational Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29366},
}

@inproceedings{aaai2024_recurrent_7080,
  title = {{Recurrent Graph Neural Networks and Their Connections to Bisimulation and Logic}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29377},
}

@inproceedings{iclr2024_equivariant_35673,
  title = {{Equivariant Matrix Function Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17400},
}

@inproceedings{iclr2025_improving_83095,
  title = {{Improving Graph Neural Networks by Learning Continuous Edge Directions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28711},
}

@inproceedings{aaai2026_permutation_107122,
  title = {{Permutation Equivariant Framelet-based Hypergraph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39474},
}

@inproceedings{icml2024_finite_44613,
  title = {{Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33839},
}

@inproceedings{icml2025_relgnn_47642,
  title = {{RelGNN: Composite Message Passing for Relational Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44955},
}

@inproceedings{neurips2024_theoretical_70235,
  title = {{Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96851},
}

@inproceedings{neurips2024_scale_70742,
  title = {{Scale Equivariant Graph Metanetworks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96382},
}

@inproceedings{neurips2024_granola_74041,
  title = {{GRANOLA: Adaptive Normalization for Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93483},
}

@inproceedings{neurips2025_diffusion_76749,
  title = {{Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=MfBw0dlBfi},
}

@inproceedings{neurips2025_generative_79855,
  title = {{Generative Graph Pattern Machine}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=tdMWo3jB21},
}

@inproceedings{aaai2025_discrete_8757,
  title = {{Discrete Curvature Graph Information Bottleneck}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33831},
}