mlsanityData to 14 Oct 2026

Graph Signal Adaptive Message Passing

arXiv 2410.17629 ↗First posted 23 Oct 2024eess.SP cs.AIcited by 1

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Abstract (arXiv)

This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Graph Signal Processing methods that apply the same filter to the entire graph, the spatiotemporal updates of GSAMP employ a distinct approach that utilizes localized computations at each node. This update is based on an adaptive solution obtained from an optimization problem designed to minimize the discrepancy between observed and estimated values. GSAMP effectively processes real-world, time-varying graph signals under Gaussian and impulsive noise conditions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 48 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

27 of 48 references.

References · 27

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  8. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  9. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  10. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  11. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  12. 2020Graph signal processing for machine learning: A review and new perspectivescited by 73
15 more
  1. 2021Simplicial Convolutional Neural Networkscited by 40
  2. 2023Graph Signal Processing: History, Development, Impact, and Outlookcited by 39
  3. 2023Permutation Equivariant Graph Framelets for Heterophilous Graph Learningcited by 19
  4. 2022Reconstruction of Time-varying Graph Signals via Sobolev Smoothnesscited by 18
  5. 2022HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classificationcited by 18
  6. 2022Laplacian Convolutional Representation for Traffic Time Series Imputationcited by 18
  7. 2016Adaptive Least Mean Squares Estimation of Graph Signalscited by 13
  8. 2022Adaptive Sign Algorithm for Graph Signal Processingcited by 11
  9. 2023Sequential Monte Carlo Graph Convolutional Network for Dynamic Brain Connectivitycited by 6
  10. 2022Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noisecited by 4
  11. 2023Time-varying Signals Recovery via Graph Neural Networkscited by 4
  12. 2024Binarized Simplicial Convolutional Neural Networkscited by 3
  13. 2022Adaptive Joint Estimation of Temporal Vertex and Edge Signalscited by 2
  14. 2020Statistical Graph Signal Recovery Using Variational Bayescited by 1
  15. 2022Proportionate Adaptive Graph Signal Recoverycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Graph Mamba Operator: A Latent Simulator for Interacting Particle Systemscited by 0