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

@inproceedings{cvpr2017_alternating_12054,
  title = {{Alternating Direction Graph Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2017_a_12027,
  title = {{A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2019_nearly_57791,
  title = {{(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13959},
}

@inproceedings{cvpr2023_progressive_21925,
  title = {{Progressive Neighbor Consistency Mining for Correspondence Pruning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23007},
}

@inproceedings{cvpr2016_tensor_11917,
  title = {{Tensor Power Iteration for Multi-Graph Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2021_efficient_16934,
  title = {{Efficient Initial Pose-Graph Generation for Global SfM}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2018_generalizing_57164,
  title = {{Generalizing Graph Matching beyond Quadratic Assignment Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11107},
}

@inproceedings{cvpr2017_binary_12088,
  title = {{Binary Constraint Preserving Graph Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2017_a_12006,
  title = {{A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{icml2020_spectral_38779,
  title = {{Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6051},
}

@inproceedings{aaai2017_nonnegative_103931,
  title = {{Nonnegative Orthogonal Graph Matching}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11210},
}

@inproceedings{aaai2021_gene_657,
  title = {{Gene Regulatory Network Inference as Relaxed Graph Matching}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10263-gene-regulatory-network-inference-as-relaxed-graph-matching/},
}

@inproceedings{neurips2017_graph_56352,
  title = {{Graph Matching via Multiplicative Update Algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9103},
}

@inproceedings{cvpr2020_zero_16504,
  title = {{Zero-Assignment Constraint for Graph Matching With Outliers}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2017_novel_100311,
  title = {{Novel Geometric Approach for Global Alignment of PPI Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10504},
}

@inproceedings{neurips2021_correlated_62901,
  title = {{Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26493},
}

@inproceedings{cvpr2018_joint_13241,
  title = {{Joint Cuts and Matching of Partitions in One Graph}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Joint_Cuts_and_CVPR_2018_paper.html},
}

@inproceedings{cvpr2016_kinematic_11636,
  title = {{Kinematic Structure Correspondences via Hypergraph Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2018_probably_56784,
  title = {{(Probably) Concave Graph Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11065},
}

@inproceedings{neurips2019_kergm_58443,
  title = {{KerGM: Kernelized Graph Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14512},
}

@inproceedings{neurips2024_efficient_73801,
  title = {{Efficient Graph Matching for Correlated Stochastic Block Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93707},
}

@inproceedings{cvpr2025_higher_26521,
  title = {{Higher-Order Ratio Cycles for Fast and Globally Optimal Shape Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35155},
}

@inproceedings{neurips2024_harnessing_70654,
  title = {{Harnessing Multiple Correlated Networks for Exact Community Recovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96468},
}

@inproceedings{aaai2024_sat_7202,
  title = {{SAT-Based Algorithms for Regular Graph Pattern Matching}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28653},
}

@inproceedings{cvpr2025_towards_27917,
  title = {{Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33005},
}

@inproceedings{neurips2025_everybodydance_80065,
  title = {{EverybodyDance: Bipartite Graph–Based Identity Correspondence for Multi-Character Animation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vqXWCC0aUG},
}

@inproceedings{icml2024_bipartite_43857,
  title = {{Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34595},
}

@inproceedings{icml2024_robust_44575,
  title = {{Robust Graph Matching when Nodes are Corrupt}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33877},
}

@inproceedings{icml2026_neural_50322,
  title = {{Neural Minimum Weight Perfect Matching for Quantum Error Codes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=BFX213mKfh},
}

@inproceedings{cvpr2026_global_96598,
  title = {{Global-Aware Edge Prioritization for Pose Graph Initialization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Wei_Global-Aware_Edge_Prioritization_for_Pose_Graph_Initialization_CVPR_2026_paper.html},
}

@inproceedings{iclr2024_mixsatgen_34373,
  title = {{MixSATGEN: Learning Graph Mixing for SAT Instance Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18702},
}

@inproceedings{icml2024_effective_45501,
  title = {{Effective Federated Graph Matching}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32950},
}

@inproceedings{icml2026_simgfm_52788,
  title = {{SimGFM: Simplifying Discrete Flow Matching for Graph Generation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Z3xgDd5B2L},
}

@inproceedings{neurips2026_cross_95047,
  title = {{Cross-order Consensus Graph Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153663},
}