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

@inproceedings{neurips2020_open_60527,
  title = {{Open Graph Benchmark: Datasets for Machine Learning on Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16995},
}

@inproceedings{aaai2018_graph_105997,
  title = {{Graph Convolutional Networks With Argument-Aware Pooling for Event Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12039},
}

@inproceedings{cvpr2022_sigma_19771,
  title = {{SIGMA: Semantic-Complete Graph Matching for Domain Adaptive Object Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2021_uncertain_1575,
  title = {{Uncertain Graph Neural Networks for Facial  Action Unit Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/05993-uncertain-graph-neural-networks-for-facial-action-unit-detection/},
}

@inproceedings{iclr2022_discovering_31562,
  title = {{Discovering Invariant Rationales for Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6555},
}

@inproceedings{neurips2020_uncertainty_61028,
  title = {{Uncertainty Aware Semi-Supervised Learning on Graph Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18213},
}

@inproceedings{aaai2017_transfer_104527,
  title = {{Transfer Learning for Deep Learning on Graph-Structured Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10904},
}

@inproceedings{aaai2020_multi_103016,
  title = {{Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6326},
}

@inproceedings{aaai2023_non_4251,
  title = {{Non-IID Transfer Learning on Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26231},
}

@inproceedings{aaai2021_smart_1312,
  title = {{SMART: A Situation Model for Algebra Story Problems via Attributed Grammar}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/13009-smart-a-situation-model-for-algebra-story-problems-via-attributed-grammar/},
}

@inproceedings{neurips2020_graph_59968,
  title = {{Graph Policy Network for Transferable Active Learning on Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18640},
}

@inproceedings{aaai2024_data_5595,
  title = {{Data-Augmented Curriculum Graph Neural Architecture Search under Distribution Shifts}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29580},
}

@inproceedings{aaai2024_rethinking_7134,
  title = {{Rethinking Propagation for Unsupervised Graph Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29304},
}

@inproceedings{aaai2023_multi_4192,
  title = {{Multi-Domain Generalized Graph Meta Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25569},
}

@inproceedings{aaai2024_multimodal_6721,
  title = {{Multimodal Graph Neural Architecture Search under Distribution Shifts}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28663},
}

@inproceedings{iclr2022_graph_31619,
  title = {{Graph-Relational Domain Adaptation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7145},
}

@inproceedings{neurips2023_spa_69330,
  title = {{SPA: A Graph Spectral Alignment Perspective for Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70572},
}

@inproceedings{neurips2024_gft_70149,
  title = {{GFT: Graph Foundation Model with Transferable Tree Vocabulary}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96932},
}

@inproceedings{neurips2024_fedssp_71474,
  title = {{FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95786},
}

@inproceedings{neurips2021_metropolis_62694,
  title = {{Metropolis-Hastings Data Augmentation for Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27150},
}

@inproceedings{aaai2025_behavior_8240,
  title = {{Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33274},
}

@inproceedings{aaai2025_smoothness_10785,
  title = {{Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33743},
}

@inproceedings{aaai2025_traci_10913,
  title = {{TRACI: A Data-centric Approach for Multi-Domain Generalization on Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33463},
}

@inproceedings{aaai2025_virtual_11248,
  title = {{Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33830},
}

@inproceedings{icml2024_position_43881,
  title = {{Position: Graph Foundation Models Are Already Here}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34571},
}

@inproceedings{neurips2024_energy_70630,
  title = {{Energy-based Epistemic Uncertainty for Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96491},
}

@inproceedings{icml2025_multi_46040,
  title = {{Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46509},
}

@inproceedings{neurips2025_one_80207,
  title = {{One Prompt Fits All: Universal Graph Adaptation for Pretrained Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xJ2lGfFOv7},
}

@inproceedings{aaai2025_contextual_8522,
  title = {{Contextual Structure Knowledge Transfer for Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34381},
}

@inproceedings{aaai2025_domain_8797,
  title = {{Domain Adaptive Unfolded Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34431},
}

@inproceedings{aaai2025_unified_11158,
  title = {{Unified Graph Neural Networks Pre-training for Multi-domain Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33325},
}

@inproceedings{neurips2025_graph_74889,
  title = {{Graph Data Selection for Domain Adaptation: A Model-Free Approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2ExVHRE6jd},
}

@inproceedings{neurips2025_graph_79927,
  title = {{Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=uIQX0IA8nq},
}

@inproceedings{aaai2026_graph_106895,
  title = {{Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39247},
}

@inproceedings{aaai2025_core_8541,
  title = {{Core Knowledge Learning Framework for Graph}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33438},
}