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

@inproceedings{neurips2017_geometric_56344,
  title = {{Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9152},
}

@inproceedings{aaai2017_matching_104462,
  title = {{Matching Node Embeddings for Graph Similarity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10839},
}

@inproceedings{neurips2016_toward_56054,
  title = {{Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7402},
}

@inproceedings{neurips2018_watch_57776,
  title = {{Watch Your Step: Learning Node Embeddings via Graph Attention}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11875},
}

@inproceedings{neurips2016_on_55907,
  title = {{On Valid Optimal Assignment Kernels and Applications to Graph Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7117},
}

@inproceedings{neurips2016_the_56041,
  title = {{The Multiscale Laplacian Graph Kernel}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7417},
}

@inproceedings{cvpr2017_provable_12555,
  title = {{Provable Self-Representation Based Outlier Detection in a Union of Subspaces}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2017_hunt_56367,
  title = {{Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8807},
}

@inproceedings{aaai2017_large_104498,
  title = {{Large Graph Hashing with Spectral Rotation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10875},
}

@inproceedings{cvpr2016_robust_11824,
  title = {{Robust Visual Place Recognition With Graph Kernels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2019_wasserstein_59192,
  title = {{Wasserstein Weisfeiler-Lehman Graph Kernels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14541},
}

@inproceedings{icml2018_hyperbolic_36739,
  title = {{Hyperbolic Entailment Cones for Learning Hierarchical Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2487},
}

@inproceedings{icml2019_geometrics_37384,
  title = {{GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3656},
}

@inproceedings{neurips2019_a_57846,
  title = {{A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13853},
}

@inproceedings{neurips2016_structured_56022,
  title = {{Structured Prediction Theory Based on Factor Graph Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7294},
}

@inproceedings{icml2019_hyperbolic_37432,
  title = {{Hyperbolic Disk Embeddings for Directed Acyclic Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4043},
}

@inproceedings{neurips2019_guided_58360,
  title = {{Guided Similarity Separation for Image Retrieval}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13217},
}

@inproceedings{aaai2020_scalable_102181,
  title = {{Scalable Probabilistic Matrix Factorization with Graph-Based Priors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6043},
}

@inproceedings{aaai2021_movie_1011,
  title = {{Movie Summarization via Sparse Graph Construction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/13631-movie-summarization-via-sparse-graph-construction/},
}

@inproceedings{cvpr2017_non_12495,
  title = {{Non-Uniform Subset Selection for Active Learning in Structured Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2025_geomm_26431,
  title = {{GeoMM: On Geodesic Perspective for Multi-modal Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34184},
}

@inproceedings{icml2024_gaussian_44775,
  title = {{Gaussian Processes on Cellular Complexes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33677},
}

@inproceedings{iclr2025_neural_83719,
  title = {{Neural Spacetimes for DAG Representation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28100},
}

@inproceedings{aaai2024_principle_6987,
  title = {{Principle Component Trees and Their Persistent Homology}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29222},
}

@inproceedings{neurips2024_graphcode_71908,
  title = {{Graphcode: Learning from multiparameter persistent homology using graph neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95396},
}

@inproceedings{neurips2024_continuous_73360,
  title = {{Continuous Partitioning for Graph-Based Semi-Supervised Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94088},
}

@inproceedings{aaai2025_wate_11267,
  title = {{WatE: A Wasserstein t-distributed Embedding Method for Information-enriched Graph Visualization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33758},
}

@inproceedings{iclr2024_a_35124,
  title = {{A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17949},
}

@inproceedings{icml2024_optimal_43618,
  title = {{Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34834},
}

@inproceedings{icml2024_convergence_45766,
  title = {{Convergence Guarantees for the DeepWalk Embedding on Block Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32686},
}

@inproceedings{icml2025_cover_48097,
  title = {{Cover learning for large-scale topology representation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44511},
}

@inproceedings{icml2025_pieclam_48869,
  title = {{PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43754},
}

@inproceedings{icml2026_unfolded_50570,
  title = {{Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=DjnbMWE0Ek},
}

@inproceedings{icml2026_collapsed_52981,
  title = {{Collapsed Effective Operators for Higher-order Structures}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=arc2pWtZLN},
}

@inproceedings{neurips2024_fast_71224,
  title = {{Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96014},
}

@inproceedings{neurips2024_learning_71407,
  title = {{Learning Representations for Hierarchies with Minimal Support}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95845},
}

@inproceedings{neurips2025_hierarchical_75336,
  title = {{Hierarchical Shortest-Path Graph Kernel Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=721bDIvjen},
}

@inproceedings{neurips2025_a_77304,
  title = {{A Few Moments Please: Scalable Graphon Learning via Moment Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=STKDn1LBam},
}

@inproceedings{neurips2025_spectral_78015,
  title = {{Spectral Graph Coarsening Using Inner Product Preservation and the Grassmann Manifold}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=aBUG2Phwdt},
}

@inproceedings{iclr2025_node_81451,
  title = {{Node Similarities under Random Projections: Limits and Pathological Cases}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30323},
}