mlsanityData to 14 Oct 2026

LON-GNN: Spectral GNNs with Learnable Orthonormal Basis

arXiv 2303.13750 ↗First posted 24 Mar 2023cs.LG cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks. Although various kinds of polynomial bases have been explored, each such method adopts a fixed polynomial basis which might not be the optimal choice for the given graph. Besides, we identify the so-called over-passing issue of these methods and show that it is somewhat rooted in their less-principled regularization strategy and unnormalized basis. In this paper, we make the first attempts to address these two issues. Leveraging Jacobi polynomials, we design a novel spectral GNN, LON-GNN, with Learnable OrthoNormal bases and prove that regularizing coefficients becomes equivalent to regularizing the norm of learned filter function now. We conduct extensive experiments on diverse graph datasets to evaluate the fitting and generalization capability of LON-GNN, where the results imply its superiority.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 24 Mar 2023.

Builds on

35 of 45 references.

References · 35

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  6. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  7. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  8. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  9. 2019Simplifying Graph Convolutional Networkscited by 1,477
  10. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  11. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  12. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 881
23 more
  1. 2018Link Prediction Based on Graph Neural Networkscited by 832
  2. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  3. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  4. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
  5. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  6. 2019Multi-scale Attributed Node Embeddingcited by 548
  7. 2019Graph Transformer Networkscited by 523
  8. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  9. 2020Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingcited by 313
  10. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  11. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  12. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  13. 2019Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernelscited by 161
  14. 2019On the Universality of Invariant Networkscited by 152
  15. 2016Discriminative Embeddings of Latent Variable Models for Structured Datacited by 131
  16. 2018Attention-based Graph Neural Network for Semi-supervised Learningcited by 101
  17. 2021Breaking the Limits of Message Passing Graph Neural Networkscited by 101
  18. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  19. 2021Graph Neural Networks Inspired by Classical Iterative Algorithmscited by 60
  20. 2021Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depthcited by 55
  21. 2021Graph Neural Networks with Local Graph Parameterscited by 52
  22. 2022EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networkscited by 26
  23. 2020Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networkscited by 15

Built on by

Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionscited by 64
  2. 2024Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filteringcited by 4
  3. 2024Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approachcited by 1
  4. 2024Deep Frequency-Aware Functional Maps for Robust Shape Matchingcited by 0