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Geometric Scattering for Graph Data Analysis

arXiv 1810.03068 ↗First posted 7 Oct 2018cs.LG stat.MLcited by 49

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Abstract (arXiv)

We explore the generalization of scattering transforms from traditional (e.g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep learning, and the utility of extracted graph features in graph data analysis. In particular, we focus on the capacity of these features to retain informative variability and relations in the data (e.g., between individual graphs, or in aggregate), while relating our construction to previous theoretical results that establish the stability of similar transforms to families of graph deformations. We demonstrate the application the our geometric scattering features in graph classification of social network data, and in data exploration of biochemistry data.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Oct 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 10; 2023: 11; 2024: 6; 2025: 4; 2026: 5.

49 papers.

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  1. 2021Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?cited by 82
  2. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
  3. 2022Unlearning Graph Classifiers with Limited Data Resourcescited by 31
  4. 2022Skeleton-Parted Graph Scattering Networks for 3D Human Motion Predictioncited by 29
  5. 2022Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?cited by 25
  6. 2021The Shapley Value of Classifiers in Ensemble Gamescited by 23
  7. 2021Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNscited by 18
  8. 2022Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networkscited by 18
  9. 2022Group-invariant max filteringcited by 18
  10. 2022Geometric Scattering on Measure Spacescited by 17
  11. 2021Parametric Scattering Networkscited by 16
  12. 2021Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filteringcited by 14
37 more
  1. 2021Adaptive Gaussian Processes on Graphs via Spectral Graph Waveletscited by 11
  2. 2021Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterizationcited by 10
  3. 2022The Manifold Scattering Transform for High-Dimensional Point Cloud Datacited by 9
  4. 2022Learnable Filters for Geometric Scattering Modulescited by 9
  5. 2021Node-Variant Graph Filters in Graph Neural Networkscited by 7
  6. 2021Molecular Graph Generation via Geometric Scatteringcited by 7
  7. 2021MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapycited by 6
  8. 2021Message Passing in Graph Convolution Networks via Adaptive Filter Bankscited by 4
  9. 2024A General Graph Spectral Wavelet Convolution via Chebyshev Order Decompositioncited by 4
  10. 2023Framelet Message Passingcited by 3
  11. 2023Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Viewscited by 3
  12. 2023DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveformscited by 3
  13. 2023BLIS-Net: Classifying and Analyzing Signals on Graphscited by 3
  14. 2026Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronizationcited by 3
  15. 2023Graph Scattering beyond Wavelet Shacklescited by 2
  16. 2023Manifold Filter-Combine Networkscited by 2
  17. 2023Directed Scattering for Knowledge Graph-based Cellular Signaling Analysiscited by 2
  18. 2023Visualizing DNA reaction trajectories with deep graph embedding approachescited by 2
  19. 2025HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Datacited by 2
  20. 2022Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?cited by 1
  21. 2023ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddingscited by 1
  22. 2023Multiscale Hodge Scattering Networks for Data Analysiscited by 1
  23. 2023Domain Adaptive Graph Classificationcited by 1
  24. 2024MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learningcited by 1
  25. 2024Energy Propagation in Scattering Convolution Networks Can Be Arbitrarily Slowcited by 1
  26. 2025VDW-GNNs: Vector diffusion wavelets for geometric graph neural networkscited by 1
  27. 2026Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networkscited by 1
  28. 2021Interferometric Graph Transform for Community Labelingcited by 0
  29. 2021Spatio-Temporal Graph Complementary Scattering Networkscited by 0
  30. 2024A short review on the maximum clique problem algorithms with classical, AI, and quantum methodscited by 0
  31. 2024Convergence of Manifold Filter-Combine Networkscited by 0
  32. 2024ProtScape: A molecular structure and energy-aware representation for protein conformation generationcited by 0
  33. 2025InfoGain Wavelets: Furthering the Design of Graph Diffusion Waveletscited by 0
  34. 2025ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule miningcited by 0
  35. 2026Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physicscited by 0
  36. 2026Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?cited by 0
  37. 2026Transversal Pooling Neural Networkscited by 0