% Key papers for Theory of graph convolutional networks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2016_convolutional_55649,
  title = {{Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7008},
}

@inproceedings{icml2016_learning_35879,
  title = {{Learning Convolutional Neural Networks for Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/niepert16.html},
}

@inproceedings{cvpr2018_splinecnn_13607,
  title = {{SplineCNN: Fast Geometric Deep Learning With Continuous B-Spline Kernels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.html},
}

@inproceedings{neurips2018_combinatorial_56947,
  title = {{Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11077},
}

@inproceedings{iclr2019_lanczosnet_28795,
  title = {{LanczosNet: Multi-Scale Deep Graph Convolutional Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1099},
}

@inproceedings{icml2018_stochastic_37008,
  title = {{Stochastic Training of Graph Convolutional Networks with Variance Reduction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2169},
}

@inproceedings{iclr2019_supervised_28868,
  title = {{Supervised Community Detection with Line Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1059},
}

@inproceedings{neurips2019_layer_58464,
  title = {{Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14118},
}

@inproceedings{iclr2017_deep_28391,
  title = {{Deep Learning with Dynamic Computation Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=ryrGawqex},
}

@inproceedings{aaai2020_learning_101860,
  title = {{Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5720},
}

@inproceedings{iclr2019_graph_28866,
  title = {{Graph Wavelet Neural Network}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/710},
}

@inproceedings{aaai2018_depthlgp_99823,
  title = {{DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11271},
}

@inproceedings{cvpr2017_deepnav_12190,
  title = {{DeepNav: Learning to Navigate Large Cities}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2021_magnet_62299,
  title = {{MagNet: A Neural Network for Directed Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26382},
}

@inproceedings{neurips2020_bandit_59427,
  title = {{Bandit Samplers for Training Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18530},
}

@inproceedings{iclr2020_neural_29679,
  title = {{Neural Execution of Graph Algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SkgKO0EtvS},
}

@inproceedings{aaai2023_sigmanet_4688,
  title = {{SigMaNet: One Laplacian to Rule Them All}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25919},
}

@inproceedings{icml2017_deriving_36146,
  title = {{Deriving Neural Architectures from Sequence and Graph Kernels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/797},
}

@inproceedings{aaai2024_generating_6122,
  title = {{Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30168},
}

@inproceedings{aaai2022_early_2011,
  title = {{Early-Bird GCNs: Graph-Network Co-optimization towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08910-early-bird-gcns-graph-network-co-optimization-towards-more-efficient-gcn-training-and-inference-via-drawing-early-bird-lottery-tickets/},
}

@inproceedings{aaai2025_deepsn_8684,
  title = {{DeepSN: A Sheaf Neural Framework for Influence Maximization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33888},
}

@inproceedings{cvpr2025_hypergraph_26558,
  title = {{Hypergraph Vision Transformers: Images are More than Nodes, More than Edges}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32803},
}

@inproceedings{aaai2024_neural_6764,
  title = {{Neural Gaussian Similarity Modeling for Differential Graph Structure Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29078},
}

@inproceedings{aaai2024_graph_6156,
  title = {{Graph Learning in 4D: A Quaternion-Valued Laplacian to Enhance Spectral GCNs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29088},
}

@inproceedings{aaai2025_bi_8260,
  title = {{Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33832},
}

@inproceedings{neurips2024_continuous_72610,
  title = {{Continuous Product Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94768},
}

@inproceedings{neurips2024_interpretable_73435,
  title = {{Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94026},
}

@inproceedings{aaai2025_deep_8672,
  title = {{Deep Graph Online Hashing for Multi-Label Image Retrieval}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32191},
}

@inproceedings{neurips2024_graph_70330,
  title = {{Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96766},
}

@inproceedings{iclr2025_schur_83881,
  title = {{Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27939},
}

@inproceedings{aaai2026_neural_106347,
  title = {{Neural Graph Navigation for Intelligent Subgraph Matching}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38654},
}

@inproceedings{iclr2024_tractable_34962,
  title = {{Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18112},
}

@inproceedings{iclr2024_sum_35172,
  title = {{Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17901},
}

@inproceedings{icml2024_learning_43537,
  title = {{Learning in Deep Factor Graphs with Gaussian Belief Propagation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34915},
}

@inproceedings{icml2024_graph_44559,
  title = {{Graph Neural Networks with a Distribution of Parametrized Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33893},
}

@inproceedings{icml2025_wilting_48429,
  title = {{WILTing Trees: Interpreting the Distance Between MPNN Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44188},
}

@inproceedings{icml2026_scalable_52529,
  title = {{Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=WtINGEMjTB},
}

@inproceedings{icml2026_learning_52704,
  title = {{Learning to Execute Graph Algorithms Exactly with Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=YKEmoqwkE9},
}

@inproceedings{neurips2024_gder_71915,
  title = {{GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95389},
}