mlsanityData to 14 Oct 2026

InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets

arXiv 2504.08802 ↗First posted 8 Apr 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to various powers, known as diffusion scales. Traditionally, these scales are chosen to be dyadic integers, 2j. Here, we propose a novel, unsupervised method for selecting the diffusion scales based on ideas from information theory. We then show that our method can be incorporated into wavelet-based GNNs, which are modeled after the geometric scattering transform, via graph classification experiments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 8 Apr 2025.

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References · 27

  1. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  3. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  4. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  5. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
  6. 2022Long Range Graph Benchmarkcited by 263
  7. 2019Revisiting Graph Neural Networks: All We Have is Low-Pass Filterscited by 261
  8. 2018Geometric Scattering for Graph Data Analysiscited by 49
  9. 2015A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extractioncited by 46
  10. 2018Graph Convolutional Neural Networks via Scatteringcited by 46
  11. 2018Diffusion Scattering Transforms on Graphscited by 44
  12. 2019Stability of Graph Scattering Transformscited by 34
15 more
  1. 2020Spatio-Temporal Graph Scattering Transformcited by 19
  2. 2022Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networkscited by 18
  3. 2022Geometric Scattering on Measure Spacescited by 17
  4. 2019Understanding Graph Neural Networks with Generalized Geometric Scattering Transformscited by 12
  5. 2022The Manifold Scattering Transform for High-Dimensional Point Cloud Datacited by 9
  6. 2022Learnable Filters for Geometric Scattering Modulescited by 9
  7. 2024Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNscited by 9
  8. 2021Molecular Graph Generation via Geometric Scatteringcited by 7
  9. 2020Efficient and Stable Graph Scattering Transforms via Pruningcited by 5
  10. 2017Underwater object classification using scattering transform of sonar signalscited by 4
  11. 2024Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomicscited by 4
  12. 2022Stability of the scattering transform for deformations with minimal regularitycited by 3
  13. 2023BLIS-Net: Classifying and Analyzing Signals on Graphscited by 3
  14. 2023Manifold Filter-Combine Networkscited by 2
  15. 2023Directed Scattering for Knowledge Graph-based Cellular Signaling Analysiscited by 2

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