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

@inproceedings{aaai2016_the_105130,
  title = {{The Constrained Laplacian Rank Algorithm for Graph-Based Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10302},
}

@inproceedings{aaai2017_unsupervised_104437,
  title = {{Unsupervised Large Graph Embedding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10814},
}

@inproceedings{aaai2018_unified_102346,
  title = {{Unified Spectral Clustering With Optimal Graph}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11613},
}

@inproceedings{icml2020_spectral_38777,
  title = {{Spectral Clustering with Graph Neural Networks for Graph Pooling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6018},
}

@inproceedings{aaai2020_multi_102318,
  title = {{Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6180},
}

@inproceedings{icml2016_compressive_35766,
  title = {{Compressive Spectral Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/tremblay16.html},
}

@inproceedings{neurips2018_understanding_57738,
  title = {{Understanding Regularized Spectral Clustering via Graph Conductance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12005},
}

@inproceedings{cvpr2023_adaptive_20352,
  title = {{Adaptive Graph Convolutional Subspace Clustering}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21515},
}

@inproceedings{aaai2016_scalable_105126,
  title = {{Scalable Sequential Spectral Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10298},
}

@inproceedings{aaai2016_new_105106,
  title = {{New l1-Norm Relaxations and Optimizations for Graph Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10278},
}

@inproceedings{aaai2023_scalable_4617,
  title = {{Scalable and Effective Conductance-Based Graph Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25568},
}

@inproceedings{cvpr2022_gasp_18892,
  title = {{GASP, a Generalized Framework for Agglomerative Clustering of Signed Graphs and Its Application to Instance Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2019_revisiting_58915,
  title = {{Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13564},
}

@inproceedings{icml2019_spectral_37751,
  title = {{Spectral Clustering of Signed Graphs via Matrix Power Means}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3699},
}

@inproceedings{icml2018_spectrally_36994,
  title = {{Spectrally Approximating Large Graphs with Smaller Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1948},
}

@inproceedings{cvpr2018_local_13341,
  title = {{Local and Global Optimization Techniques in Graph-Based Clustering}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Ikami_Local_and_Global_CVPR_2018_paper.html},
}

@inproceedings{aaai2016_reduction_105090,
  title = {{Reduction Techniques for Graph-Based Convex Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10262},
}

@inproceedings{aaai2022_hypergraph_2215,
  title = {{Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel k-Means, and Heat Kernel}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08141-hypergraph-modeling-via-spectral-embedding-connection-hypergraph-cut-weighted-kernel-k-means-and-heat-kernel/},
}

@inproceedings{neurips2022_consistency_64390,
  title = {{Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53419},
}

@inproceedings{iclr2020_spectral_29423,
  title = {{Spectral Embedding of Regularized Block Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=H1l_0JBYwS},
}

@inproceedings{iclr2024_contrastive_34973,
  title = {{Contrastive Learning is Spectral Clustering on Similarity Graph}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18101},
}

@inproceedings{aaai2024_graph_6151,
  title = {{Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30479},
}

@inproceedings{aaai2024_pass_6932,
  title = {{Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30528},
}

@inproceedings{aaai2025_clustering_8426,
  title = {{Clustering by Mining Density Distributions and Splitting Manifold Structure}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35490},
}

@inproceedings{icml2024_biharmonic_43406,
  title = {{Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35046},
}

@inproceedings{icml2024_dynamic_44875,
  title = {{Dynamic Spectral Clustering with Provable Approximation Guarantee}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33577},
}

@inproceedings{icml2026_simple_52762,
  title = {{Simple Algorithms for Bad Triangle Transversals with Applications to Correlation Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Yq66fTPjHn},
}

@inproceedings{neurips2025_structure_77915,
  title = {{Structure-Aware Spectral Sparsification via Uniform Edge Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Z4eFqgYbha},
}

@inproceedings{icml2025_signed_47033,
  title = {{Signed Laplacians for Constrained Graph Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45552},
}

@inproceedings{icml2025_graph_48228,
  title = {{Graph Minimum Factor Distance and Its Application to Large-Scale Graph Data Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44384},
}

@inproceedings{icml2026_parametrized_49809,
  title = {{Parametrized Power-Iteration Clustering for Directed Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=5vI6ApLOg8},
}

@inproceedings{icml2026_edge_51276,
  title = {{Edge-colored Clustering in Hypergraphs: A MaxECC Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=KTmvzQhJqB},
}

@inproceedings{iclr2025_coreset_80585,
  title = {{Coreset Spectral Clustering}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31180},
}

@inproceedings{neurips2026_faster_90380,
  title = {{Faster Dynamic Graph Clustering with Hierarchical Graph Contraction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153729},
}