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

@inproceedings{cvpr2020_dynamic_15450,
  title = {{Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2020_distilling_15424,
  title = {{Distilling Knowledge From Graph Convolutional Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2023_simple_4693,
  title = {{Simple and Efficient Heterogeneous Graph Neural Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26283},
}

@inproceedings{iclr2020_composition_29380,
  title = {{Composition-based Multi-Relational Graph Convolutional Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BylA_C4tPr},
}

@inproceedings{aaai2021_modeling_1005,
  title = {{Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/00055-modeling-the-momentum-spillover-effect-for-stock-prediction-via-attribute-driven-graph-attention-networks/},
}

@inproceedings{cvpr2021_amalgamating_16594,
  title = {{Amalgamating Knowledge From Heterogeneous Graph Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2020_hoppity_29620,
  title = {{HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SJeqs6EFvB},
}

@inproceedings{iclr2017_learning_28255,
  title = {{Learning Graphical State Transitions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HJ0NvFzxl},
}

@inproceedings{aaai2021_joint_818,
  title = {{Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/04081-joint-air-quality-and-weather-prediction-based-on-multi-adversarial-spatiotemporal-networks/},
}

@inproceedings{iclr2020_lambdanet_29524,
  title = {{LambdaNet: Probabilistic Type Inference using Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Hkx6hANtwH},
}

@inproceedings{aaai2022_sparse_2763,
  title = {{Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/11165-sparse-structure-learning-via-graph-neural-networks-for-inductive-document-classification/},
}

@inproceedings{aaai2023_t2g_4764,
  title = {{T2G-FORMER: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26272},
}

@inproceedings{cvpr2018_graphbit_13171,
  title = {{GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Duan_GraphBit_Bitwise_Interaction_CVPR_2018_paper.html},
}

@inproceedings{aaai2021_graphmse_690,
  title = {{GraphMSE: Efficient Meta-path Selection in Semantically Aligned Feature Space for Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/04206-graphmse-efficient-meta-path-selection-in-semantically-aligned-feature-space-for-graph-neural-networks/},
}

@inproceedings{aaai2021_relation_1259,
  title = {{Relation-aware Graph Attention Model with Adaptive Self-adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09368-relation-aware-graph-attention-model-with-adaptive-self-adversarial-training/},
}

@inproceedings{iclr2020_inductive_29396,
  title = {{Inductive Matrix Completion Based on Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ByxxgCEYDS},
}

@inproceedings{cvpr2020_depara_15325,
  title = {{DEPARA: Deep Attribution Graph for Deep Knowledge Transferability}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2024_preroutgnn_6977,
  title = {{PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29653},
}

@inproceedings{icml2019_graph_37407,
  title = {{Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4004},
}

@inproceedings{aaai2021_neural_1058,
  title = {{Neural Relational Inference with Efficient Message Passing Mechanisms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07055-neural-relational-inference-with-efficient-message-passing-mechanisms/},
}

@inproceedings{aaai2024_deep_5647,
  title = {{Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28671},
}

@inproceedings{neurips2024_long_73248,
  title = {{Long-range Brain Graph Transformer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94190},
}

@inproceedings{aaai2024_mgnet_6577,
  title = {{MGNet: Learning Correspondences via Multiple Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28187},
}

@inproceedings{icml2024_position_44261,
  title = {{Position: Topological Deep Learning is the New Frontier for Relational Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34191},
}

@inproceedings{aaai2024_fair_5952,
  title = {{Fair Graph Learning Using Constraint-Aware Priority Adjustment and Graph Masking in River Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30212},
}

@inproceedings{aaai2024_an_5178,
  title = {{An Efficient Subgraph-Inferring Framework for Large-Scale Heterogeneous Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28797},
}

@inproceedings{icml2024_position_43719,
  title = {{Position: Relational Deep Learning - Graph Representation Learning on Relational Databases}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34733},
}

@inproceedings{neurips2024_4dbinfer_72693,
  title = {{4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97638},
}

@inproceedings{aaai2025_brainmap_8298,
  title = {{BrainMAP: Learning Multiple Activation Pathways in Brain Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33581},
}

@inproceedings{aaai2025_heterogeneous_9419,
  title = {{Heterogeneous Graph Neural Network on Semantic Tree}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33860},
}

@inproceedings{aaai2025_fine_9196,
  title = {{Fine-Grained Graph Representation Learning for Heterogeneous Mobile Networks with Attentive Fusion and Contrastive Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34084},
}

@inproceedings{icml2024_carte_43656,
  title = {{CARTE: Pretraining and Transfer for Tabular Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34796},
}

@inproceedings{icml2024_pgode_45167,
  title = {{PGODE: Towards High-quality System Dynamics Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33285},
}

@inproceedings{icml2025_hetssnet_46275,
  title = {{HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46285},
}

@inproceedings{neurips2024_egode_73548,
  title = {{EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93931},
}

@inproceedings{icml2024_duplex_44195,
  title = {{DUPLEX: Dual GAT for Complex Embedding of Directed Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34257},
}

@inproceedings{icml2026_what_50839,
  title = {{What Makes a Desired Graph for Relational Deep Learning?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=GDHVAs2XDQ},
}

@inproceedings{icml2026_rel_53389,
  title = {{Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=f4k0XsOlCZ},
}

@inproceedings{icml2026_fusioncell_54433,
  title = {{FusionCell: Cross-Attentive Fusion of Layout Geometry and Netlist Topology for Standard-Cell Performance Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=os54CnlmbR},
}