% Key papers for Deep graph convolution and simplification (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_graph_28675,
  title = {{Graph Attention Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/299},
}

@inproceedings{icml2019_simplifying_37744,
  title = {{Simplifying Graph Convolutional Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3683},
}

@inproceedings{aaai2018_adaptive_102424,
  title = {{Adaptive Graph Convolutional Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11691},
}

@inproceedings{iclr2020_dropedge_29523,
  title = {{DropEdge: Towards Deep Graph Convolutional Networks on Node Classification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Hkx1qkrKPr},
}

@inproceedings{aaai2020_revisiting_99729,
  title = {{Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5330},
}

@inproceedings{aaai2021_beyond_196,
  title = {{Beyond Low-frequency Information in Graph Convolutional Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/03950-beyond-low-frequency-information-in-graph-convolutional-networks/},
}

@inproceedings{iclr2018_fastgcn_28727,
  title = {{FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/145},
}

@inproceedings{iclr2020_geom_29574,
  title = {{Geom-GCN: Geometric Graph Convolutional Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=S1e2agrFvS},
}

@inproceedings{icml2020_simple_38760,
  title = {{Simple and Deep Graph Convolutional Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6111},
}

@inproceedings{cvpr2018_feastnet_13106,
  title = {{FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Verma_FeaStNet_Feature-Steered_Graph_CVPR_2018_paper.html},
}

@inproceedings{iclr2020_graphsaint_29277,
  title = {{GraphSAINT: Graph Sampling Based Inductive Learning Method}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJe8pkHFwS},
}

@inproceedings{cvpr2019_exploiting_14168,
  title = {{Exploiting Edge Features for Graph Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2020_tensor_103049,
  title = {{Tensor Graph Convolutional Networks for Text Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6359},
}

@inproceedings{neurips2018_adaptive_56841,
  title = {{Adaptive Sampling Towards Fast Graph Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11449},
}

@inproceedings{aaai2020_graph_100282,
  title = {{Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5455},
}

@inproceedings{cvpr2021_sequential_17884,
  title = {{Sequential Graph Convolutional Network for Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2022_powerful_2568,
  title = {{Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04210-powerful-graph-convolutional-networks-with-adaptive-propagation-mechanism-for-homophily-and-heterophily/},
}

@inproceedings{neurips2019_understanding_59137,
  title = {{Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14437},
}

@inproceedings{aaai2020_kriging_101856,
  title = {{Kriging Convolutional Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5716},
}

@inproceedings{neurips2020_scattering_60785,
  title = {{Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18295},
}

@inproceedings{aaai2024_gcnext_6082,
  title = {{GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28375},
}

@inproceedings{aaai2024_learning_6502,
  title = {{Learning to Approximate Adaptive Kernel Convolution on Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28291},
}

@inproceedings{aaai2024_residual_7117,
  title = {{Residual Hyperbolic Graph Convolution Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29559},
}

@inproceedings{neurips2024_unitary_73650,
  title = {{Unitary Convolutions for Learning on Graphs and Groups}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93842},
}

@inproceedings{icml2024_ckgconv_44121,
  title = {{CKGConv: General Graph Convolution with Continuous Kernels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34331},
}

@inproceedings{neurips2024_generalizing_73094,
  title = {{Generalizing CNNs to graphs with learnable neighborhood quantization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94335},
}

@inproceedings{icml2025_ficgcn_48445,
  title = {{FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44172},
}

@inproceedings{neurips2025_on_76399,
  title = {{On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=IoSLbwZkal},
}

@inproceedings{neurips2025_revolutionizing_78538,
  title = {{Revolutionizing Graph Aggregation: From Suppression to Amplification via BoostGCN}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=fmNQkLfsys},
}

@inproceedings{icml2024_on_44909,
  title = {{On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33543},
}

@inproceedings{icml2026_gi_52235,
  title = {{GI-GCN: Global Interacted Graph Convolutional Networks via Dominant Sets for Graph Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Twq39YYsul},
}

@inproceedings{iclr2025_beyond_82063,
  title = {{Beyond Random Masking: When Dropout meets Graph Convolutional Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29723},
}

@inproceedings{neurips2026_cscn_95042,
  title = {{CSCN: The Crossed Subtree Convolutional Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149718},
}