@misc{simpleconv,
      title={Graph Neural Networks with Adaptive Readouts}, 
      author={David Buterez and Jon Paul Janet and Steven J. Kiddle and Dino Oglic and Pietro Liò},
      year={2022},
      eprint={2211.04952},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{sageconv,
      title={Inductive Representation Learning on Large Graphs}, 
      author={William L. Hamilton and Rex Ying and Jure Leskovec},
      year={2018},
      eprint={1706.02216},
      archivePrefix={arXiv},
      primaryClass={cs.SI}
}

@misc{graphconv,
      title={Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks}, 
      author={Christopher Morris and Martin Ritzert and Matthias Fey and William L. Hamilton and Jan Eric Lenssen and Gaurav Rattan and Martin Grohe},
      year={2021},
      eprint={1810.02244},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{resgatedgraphconv,
      title={Residual Gated Graph ConvNets}, 
      author={Xavier Bresson and Thomas Laurent},
      year={2018},
      eprint={1711.07553},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{gatconv,
      title={Graph Attention Networks}, 
      author={Petar Veličković and Guillem Cucurull and Arantxa Casanova and Adriana Romero and Pietro Liò and Yoshua Bengio},
      year={2018},
      eprint={1710.10903},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{gatv2conv,
      title={How Attentive are Graph Attention Networks?}, 
      author={Shaked Brody and Uri Alon and Eran Yahav},
      year={2022},
      eprint={2105.14491},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{transformerconv,
      title={Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification}, 
      author={Yunsheng Shi and Zhengjie Huang and Shikun Feng and Hui Zhong and Wenjin Wang and Yu Sun},
      year={2021},
      eprint={2009.03509},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{ginconv,
      title={How Powerful are Graph Neural Networks?}, 
      author={Keyulu Xu and Weihua Hu and Jure Leskovec and Stefanie Jegelka},
      year={2019},
      eprint={1810.00826},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{mfconv,
      title={Convolutional Networks on Graphs for Learning Molecular Fingerprints}, 
      author={David Duvenaud and Dougal Maclaurin and Jorge Aguilera-Iparraguirre and Rafael Gómez-Bombarelli and Timothy Hirzel and Alán Aspuru-Guzik and Ryan P. Adams},
      year={2015},
      eprint={1509.09292},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{cgconv,
  title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties},
  author = {Xie, Tian and Grossman, Jeffrey C.},
  journal = {Phys. Rev. Lett.},
  volume = {120},
  issue = {14},
  pages = {145301},
  numpages = {6},
  year = {2018},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.120.145301},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301}
}

@misc{edgeconv,
      title={Dynamic Graph CNN for Learning on Point Clouds}, 
      author={Yue Wang and Yongbin Sun and Ziwei Liu and Sanjay E. Sarma and Michael M. Bronstein and Justin M. Solomon},
      year={2019},
      eprint={1801.07829},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{feastconv,
      title={FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis}, 
      author={Nitika Verma and Edmond Boyer and Jakob Verbeek},
      year={2018},
      eprint={1706.05206},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{leconv,
      title={ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations}, 
      author={Ekagra Ranjan and Soumya Sanyal and Partha Pratim Talukdar},
      year={2020},
      eprint={1911.07979},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{genconv,
      title={DeeperGCN: All You Need to Train Deeper GCNs}, 
      author={Guohao Li and Chenxin Xiong and Ali Thabet and Bernard Ghanem},
      year={2020},
      eprint={2006.07739},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{wlconvcontinuous,
      title={Wasserstein Weisfeiler-Lehman Graph Kernels}, 
      author={Matteo Togninalli and Elisabetta Ghisu and Felipe Llinares-López and Bastian Rieck and Karsten Borgwardt},
      year={2019},
      eprint={1906.01277},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{movielens,
  author    = {F. Maxwell Harper and Joseph A. Konstan},
  title     = {The MovieLens Datasets: History and Context},
  journal   = {ACM Transactions on Interactive Intelligent Systems (TiiS)},
  volume    = {5},
  number    = {4},
  year      = {2015},
  pages     = {19:1--19:19},
  doi       = {10.1145/2827872},
}

@misc{gineconv,
      title={Strategies for Pre-training Graph Neural Networks}, 
      author={Weihua Hu and Bowen Liu and Joseph Gomes and Marinka Zitnik and Percy Liang and Vijay Pande and Jure Leskovec},
      year={2020},
      eprint={1905.12265},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{gmmconv,
      title={Geometric deep learning on graphs and manifolds using mixture model CNNs}, 
      author={Federico Monti and Davide Boscaini and Jonathan Masci and Emanuele Rodolà and Jan Svoboda and Michael M. Bronstein},
      year={2016},
      eprint={1611.08402},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{splineconv,
      title={SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels}, 
      author={Matthias Fey and Jan Eric Lenssen and Frank Weichert and Heinrich Müller},
      year={2018},
      eprint={1711.08920},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{nnconv,
      title={Neural Message Passing for Quantum Chemistry}, 
      author={Justin Gilmer and Samuel S. Schoenholz and Patrick F. Riley and Oriol Vinyals and George E. Dahl},
      year={2017},
      eprint={1704.01212},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{generalconv,
      title={Design Space for Graph Neural Networks}, 
      author={Jiaxuan You and Rex Ying and Jure Leskovec},
      year={2021},
      eprint={2011.08843},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{faconv,
      title={Beyond Low-frequency Information in Graph Convolutional Networks}, 
      author={Deyu Bo and Xiao Wang and Chuan Shi and Huawei Shen},
      year={2021},
      eprint={2101.00797},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{nnmips1,
  title={Product quantization for nearest neighbor search},
  author={J{\'e}gou, Herv{\'e} and Douze, Matthijs and Schmid, Cordelia},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={33},
  number={1},
  pages={117--128},
  year={2011},
  publisher={IEEE}
}

@misc{nnmips2,
      title={Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search}, 
      author={Shicong Liu and Hongtao Lu and Junru Shao},
      year={2015},
      eprint={1509.05195},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{nnmips3,
  title={Efficient K-Nearest Neighbor Graph Construction for Generic Similarity Measures},
  author={Dong, Wei and Charikar, Moses and Li, Kai},
  booktitle={Proceedings of the 20th International Conference on World Wide Web},
  pages={577--586},
  year={2011},
  organization={ACM}
}

@misc{faiss,
      title={The Faiss library}, 
      author={Matthijs Douze and Alexandr Guzhva and Chengqi Deng and Jeff Johnson and Gergely Szilvasy and Pierre-Emmanuel Mazaré and Maria Lomeli and Lucas Hosseini and Hervé Jégou},
      year={2024},
      eprint={2401.08281},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{transformer,
      title={Attention Is All You Need}, 
      author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
      year={2023},
      eprint={1706.03762},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}