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@misc{braxton2018,
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@misc{cascade,
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@inproceedings{barbarossa2018learning,
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@article{perraudin2017stationarity,
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@article{wu2020survey,  author={Z. {Wu} and S. {Pan} and F. {Chen} and G. {Long} and C. {Zhang} and P. S. {Yu}},
 journal={IEEE Transactions on Neural Networks and Learning Systems},   title={A Comprehensive Survey on Graph Neural Networks},
 year={2020},
  volume={},
  number={},
  pages={1-21},}

@article{gilmer2017NeuralMP,
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@article{battaglia2018relational,
title	= {Relational inductive biases, deep learning, and graph networks},
author	= {Peter Battaglia and Jessica Blake Chandler Hamrick and Victor Bapst and Alvaro Sanchez and Vinicius Zambaldi and Mateusz Malinowski and Andrea Tacchetti and David Raposo and Adam Santoro and Ryan Faulkner and Caglar Gulcehre and Francis Song and Andy Ballard and Justin Gilmer and George E. Dahl and Ashish Vaswani and Kelsey Allen and Charles Nash and Victoria Jayne Langston and Chris Dyer and Nicolas Heess and Daan Wierstra and Pushmeet Kohli and Matt Botvinick and Oriol Vinyals and Yujia Li and Razvan Pascanu},
year	= {2018},
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eprint = {1806.01261},
}


@incollection{defferrard2016convolutional,
title = {Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering},
author = {Defferrard, Micha\"{e}l and Bresson, Xavier and Vandergheynst, Pierre},
booktitle = {Advances in Neural Information Processing Systems 29},
pages = {3844--3852},
year = {2016},

}


@inproceedings{feng2018hypergraphs,
author = {Feng, Yifan and You, Haoxuan and Zizhao, Zhang and Ji, Rongrong and Gao, Yue},
year = {2019},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={33},
number={01},
title = {Hypergraph Neural Networks}
}

@ARTICLE{bronstein2017geometric,

  author={M. M. {Bronstein} and J. {Bruna} and Y. {LeCun} and A. {Szlam} and P. {Vandergheynst}},
  journal={IEEE Signal Processing Magazine},
  title={Geometric Deep Learning: Going beyond Euclidean data},
  year={2017},
  volume={34},
  number={4},
  pages={18-42}}


@InProceedings{Gabrielsson2020topological,
title = {A Topology Layer for Machine Learning},
author = {Gabrielsson, Rickard Br\"uel and Nelson, Bradley J. and Dwaraknath, Anjan and Skraba, Primoz},
pages = {1553--1563},
year = {2020}, 
series = {Proceedings of Machine Learning Research},
volume = {108}
}

@article{Hofer2019LearningRO,
  title={Learning Representations of Persistence Barcodes},
  author={C. Hofer and R. Kwitt and M. Niethammer},
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  year={2019},
  volume={20},
  pages={126:1-126:45}
}

@InProceedings{rieck2018neural,
author = {Rieck, Bastian and Togninalli, Matteo and Bock, Christian and Moor, Michael and Horn, Max and Gumbsch, Thomas and Borgwardt, Karsten},
year = {2019},
series = {International Conference on Learning Representations},

title = {Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology}
}



@inproceedings{ammar18NAACL,
title={Construction of the Literature Graph in Semantic Scholar},
author={Waleed Ammar and Dirk Groeneveld and Chandra Bhagavatula and Iz Beltagy and Miles Crawford and Doug and Sam Skjonsberg and Lucy Lu Wang and Chris Wilhelm and Zheng Yuan and Madeleine van Zuylen and Oren Etzioni},
booktitle={NAACL},
year={2018},
url={https://www.semanticscholar.org/paper/09e3cf5704bcb16e6657f6ceed70e93373a54618}
}


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@article{spinelli2020neural,
title = {Missing data imputation with adversarially-trained graph convolutional networks},
author = {Indro Spinelli and Simone Scardapane and Aurelio Uncini},
journal = {Neural Networks},
volume = {129},
pages = {249 - 260},
year = {2020}
}


@inproceedings{morris2019weisfeiler,
author = {Morris, Christopher and Ritzert, Martin and Fey, Matthias and Hamilton, William and Lenssen, Jan and Rattan, Gaurav and Grohe, Martin},
year = {2019},
booktitle={Proc. AAAI},
title = {Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks}
}

%% Structure in NNs: sphere, group, graphs

@article{perraudin2019deepsphere,
  title={{DeepSphere:} Efficient spherical convolutional neural network with {HEALPix} sampling for cosmological applications},
  author={Perraudin, Nathana{\"e}l and Defferrard, Micha{\"e}l and Kacprzak, Tomasz and Sgier, Raphael},
  journal={Astronomy and Computing},
  volume={27},
  pages={130--146},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{defferrard2020deepsphere,
  title = {{DeepSphere}: a graph-based spherical {CNN}},
  author = {Defferrard, Michaël and Milani, Martino and Gusset, Frédérick and Perraudin, Nathanaël},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1e3OlStPB},
}

@inproceedings{cohen2018sphericalcnn,
  title = {Spherical CNNs},
  author = {Cohen, Taco S and Geiger, Mario and Koehler, Jonas and Welling, Max},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  archivePrefix = {arXiv},
  eprint = {1801.10130},
}

@inproceedings{esteves2018sphericalcnn,
  title = {Learning SO(3) Equivariant Representations with Spherical CNNs},
  author = {Esteves, Carlos and Allen-Blanchette, Christine and Makadia, Ameesh and Daniilidis, Kostas},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year = {2018},
  archivePrefix = {arXiv},
  eprint = {1711.06721},
}

@article{cohen2019general,
      title = {A General Theory of Equivariant CNNs on Homogeneous  Spaces},
      author = {Cohen, Taco S. and Geiger, Mario and Weiler, Maurice},
      journal = {Advances In Neural Information Processing Systems 32 (Nips  2019)},
      volume = {32},
      series = {Advances in Neural Information Processing Systems},
      year = {2019},

}

@InProceedings{cohen2016groupnn,
 title = {Group Equivariant Convolutional Networks},
 author = {Taco Cohen and Max Welling},
 pages = {2990--2999},
 year = {2016},
 volume = {48},
 series = {Proceedings of Machine Learning Research},
}

@article{kondor2018groupnn,
  title={On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups},
  author={Kondor, Risi and Trivedi, Shubhendu},
  booktitle={International Conference on Machine Learning},
  pages={2747--2755},
  year={2018},
  archivePrefix = {arXiv},
  eprint = {1802.03690},
}

@inproceedings{worrall2017harmonicnn,
  title={Harmonic networks: Deep translation and rotation equivariance},
  author={Worrall, Daniel E and Garbin, Stephan J and Turmukhambetov, Daniyar and Brostow, Gabriel J},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5028--5037},
  year={2017}
}

@inproceedings{bruna2014graphnn,
  author = {Bruna, Joan and Zaremba, Wojciech and Szlam, Arthur and LeCun,  Yann},
  title = {Spectral Networks and Locally Connected Networks on Graphs},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2014},
  archivePrefix = {arXiv},
  eprint = {1312.6203},
}

%% Higher-order relations

@article{ze2020graph,
  title={Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network},
  author={ Ze Xiao and Yue Deng },
  journal={PLoS One},
  volume={15},
  year={2020},

}

@INPROCEEDINGS{monti2018motif,
  author={F. {Monti} and K. {Otness} and M. M. {Bronstein}},
  booktitle={2018 IEEE Data Science Workshop (DSW)},
  title={MotifNet: a motif-based Graph Convolutional Network for directed graphs},
  year={2018},
  volume={},
  number={},
  pages={225-228},}