mlsanityData to 14 Oct 2026

Framelet Message Passing

arXiv 2302.14806 ↗First posted 28 Feb 2023cs.LG cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs) have achieved champion in wide applications. Neural message passing is a typical key module for feature propagation by aggregating neighboring features. In this work, we propose a new message passing based on multiscale framelet transforms, called Framelet Message Passing. Different from traditional spatial methods, it integrates framelet representation of neighbor nodes from multiple hops away in node message update. We also propose a continuous message passing using neural ODE solvers. It turns both discrete and continuous cases can provably achieve network stability and limit oversmoothing due to the multiscale property of framelets. Numerical experiments on real graph datasets show that the continuous version of the framelet message passing significantly outperforms existing methods when learning heterogeneous graphs and achieves state-of-the-art performance on classic node classification tasks with low computational costs.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 57 of its 80 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

57 of 80 references.

References · 57

  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. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  7. 2019Simplifying Graph Convolutional Networkscited by 1,477
  8. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  9. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  10. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  11. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  12. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
38 more
  1. 2020Simple and Deep Graph Convolutional Networkscited by 860
  2. 2021How Attentive are Graph Attention Networks?cited by 835
  3. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  4. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  5. 2019Multi-scale Attributed Node Embeddingcited by 548
  6. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  7. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 362
  8. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
  9. 2019Revisiting Graph Neural Networks: All We Have is Low-Pass Filterscited by 261
  10. 2021GRAND: Graph Neural Diffusioncited by 258
  11. 2020A Note on Over-Smoothing for Graph Neural Networkscited by 249
  12. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 221
  13. 2020Graph Random Neural Network for Semi-Supervised Learning on Graphscited by 213
  14. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  15. 2019Graph Neural Ordinary Differential Equationscited by 154
  16. 2020Directional Graph Networkscited by 133
  17. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  18. 2019Stability Properties of Graph Neural Networkscited by 127
  19. 2020From Local Structures to Size Generalization in Graph Neural Networkscited by 124
  20. 2020Graphon Neural Networks and the Transferability of Graph Neural Networkscited by 118
  21. 2019Continuous Graph Neural Networkscited by 114
  22. 2020Towards Deeper Graph Neural Networks with Differentiable Group Normalizationcited by 114
  23. 2021Breaking the Limits of Message Passing Graph Neural Networkscited by 101
  24. 2019Stability and Generalization of Graph Convolutional Neural Networkscited by 81
  25. 2020Graph Neural Networks: Architectures, Stability and Transferabilitycited by 80
  26. 2022Expander Graph Propagationcited by 75
  27. 2021Graph Neural Networks Inspired by Classical Iterative Algorithmscited by 60
  28. 2022Generalization Analysis of Message Passing Neural Networks on Large Random Graphscited by 59
  29. 2022Oversquashing in GNNs through the lens of information contraction and graph expansioncited by 53
  30. 2018Geometric Scattering for Graph Data Analysiscited by 49
  31. 2021How Framelets Enhance Graph Neural Networkscited by 46
  32. 2022Rewiring with Positional Encodings for Graph Neural Networkscited by 36
  33. 2019On the Transferability of Spectral Graph Filterscited by 28
  34. 2021Interpretable Stability Bounds for Spectral Graph Filterscited by 28
  35. 2020On the Stability of Graph Convolutional Neural Networks under Edge Rewiringcited by 25
  36. 2020Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Frameworkcited by 24
  37. 2019Fast Haar Transforms for Graph Neural Networkscited by 20
  38. 2021Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphscited by 18

50 most cited of 57 All 57 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyondcited by 13
  2. 2024Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictionscited by 2
  3. 2023A Unified View on Neural Message Passing with Opinion Dynamics for Social Networkscited by 1