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• Awesome Spectral Graph Neural Networks

Awesome Spectral Graph Neural Networks

Contents

• Survey Papers
• Milestone Papers
• Spatial and Spectral Views
• Twin Papers
• Applications
• Code
• Citation

Surveys

• Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks.
  • Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu.
  • ACM Computer Surveys (CSUR), 2023
• A Survey on Spectral Graph Neural Networks.
  • Bo, Deyu, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, and Chuan Shi.
  • arXiv preprint arXiv:2302.05631 (2023)
• Transferability of spectral graph convolutional neural networks.
  • Levie, Ron, Wei Huang, Lorenzo Bucci, Michael Bronstein, and Gitta Kutyniok.
  • The Journal of Machine Learning Research (JMLR) 22, no. 1 (2021): 12462-12520.
• Geometric deep learning: going beyond euclidean data.
  • Bronstein, Michael M., Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst.
  • IEEE Signal Processing Magazine (SPM) 34, no. 4 (2017): 18-42.
• Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory.
  • A. Salim and S. Sumitra
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
• Bridging the gap between spectral and spatial domains in graph neural networks.
  • Balcilar, Muhammet, Guillaume Renton, Pierre Héroux, Benoit Gauzere, Sebastien Adam, Paul Honeine.
  • arXiv preprint arXiv:2003.11702 (2020).
• Understanding spectral graph neural network.
  • Chen, Xinye.
  • arXiv preprint arXiv:2012.06660 (2020).

Milestone Papers in Building Spectral Graph Convolution

• Wavelets on graphs via spectral graph theory
  • David K. Hammond, Pierre Vandergheynst, Rémi Gribonval,
  • Applied and Computational Harmonic Analysis, 2010
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
  • Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst
  • Advances in Neural Information Processing Systems (NIPS), 2016
• The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
  • D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega and P. Vandergheynst
  • IEEE Signal Processing Magazine, 2013

GNNs in Spatial and Spectral Views

Twin Papers

Applications with Spectral Graph

• “Graph frequency analysis of brain signals.”
  • Huang, Weiyu, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro.
  • IEEE journal of selected topics in signal processing 10, no. 7 (2016): 1189-1203.
• “Spectral graph convolutions for population-based disease prediction.”
  • Parisot, Sarah, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, and Daniel Rueckert.
  • In Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, pp. 177-185. Springer International Publishing, 2017.
• “Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease.”
  • Parisot, Sarah, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, and Daniel Rueckert.
  • Medical image analysis 48 (2018): 117-130.
• “Spectral graph theory of brain oscillations.”
  • Raj, Ashish, Chang Cai, Xihe Xie, Eva Palacios, Julia Owen, Pratik Mukherjee, and Srikantan Nagarajan.
  • Human brain mapping 41, no. 11 (2020): 2980-2998.
• “Spectral graph theory of brain oscillations—-Revisited and improved.”
  • Verma, Parul, Srikantan Nagarajan, and Ashish Raj.
  • NeuroImage 249 (2022): 118919.
• “A spectral graph regression model for learning brain connectivity of Alzheimer’s disease.”
  • Hu, Chenhui, Lin Cheng, Jorge Sepulcre, Keith A. Johnson, Georges E. Fakhri, Yue M. Lu, and Quanzheng Li.
  • PloS one 10, no. 5 (2015): e0128136.
• “Stability and dynamics of a spectral graph model of brain oscillations.”
  • Hu, Chenhui, Lin Cheng, Jorge Sepulcre, Keith A. Johnson, Georges E. Fakhri, Yue M. Lu, and Quanzheng Li.
  • Network Neuroscience 7, no. 1 (2023): 48-72.
• “Learning spectral graph transformations for link prediction.”
  • Kunegis, Jérôme, and Andreas Lommatzsch.
  • In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 561-568. 2009.
• “Spectral graph analysis of the geometry of power flows in transmission networks.”
  • Retiére, Nicolas, Dinh Truc Ha, and Jean-Guy Caputo.
  • IEEE Systems Journal 14, no. 2 (2019): 2736-2747.
• “Threshold selection in gene co-expression networks using spectral graph theory techniques.”
  • Perkins, Andy D., and Michael A. Langston.
  • In BMC bioinformatics, vol. 10, no. 11, pp. 1-11. BioMed Central, 2009.
• “Graph spectral analysis of protein interaction network evolution.”
  • Thorne, Thomas, and Michael PH Stumpf.
  • Journal of The Royal Society Interface 9, no. 75 (2012): 2653-2666.
• “Investigating transport network vulnerability by capacity weighted spectral analysis.”
  • Bell, Michael GH, Fumitaka Kurauchi, Supun Perera, and Walter Wong.
  • Transportation Research Part B: Methodological 99 (2017): 251-266.
• “Spectral graph convolutions for population-based disease prediction.”
  • Parisot, Sarah, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, and Daniel Rueckert.
  • In Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, pp. 177-185. Springer International Publishing, 2017.
• “Epidemic spreading in real networks: An eigenvalue viewpoint.”
  • Wang, Yang, Deepayan Chakrabarti, Chenxi Wang, and Christos Faloutsos.
  • In 22nd International Symposium on Reliable Distributed Systems, 2003. Proceedings., pp. 25-34. IEEE, 2003.

Experiment Code for Benchmarking Rational vs Polynomial

See code folder

Citation Info

@article{10.1145/3627816,
    author = {Chen, Zhiqian and Chen, Fanglan and Zhang, Lei and Ji, Taoran and Fu, Kaiqun and Zhao, Liang and Chen, Feng and Wu, Lingfei and Aggarwal, Charu and Lu, Chang-Tien},
    title = {Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks},
    year = {2023},
    issue_date = {May 2024},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {56},
    number = {5},
    issn = {0360-0300},
    url = {https://doi.org/10.1145/3627816},
    doi = {10.1145/3627816},
    journal = {ACM Comput. Surv.},
    month = {dec},
    articleno = {126},
    numpages = {42},
    keywords = {approximation theory, spectral graph theory, Deep learning, graph neural networks, graph learning}
}