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Graph Signal Processing: Overview, Challenges and Applications

arXiv 1712.00468 ↗First posted 1 Dec 2017eess.SPcited by 413

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Abstract (arXiv)

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.

Key facts. Cited by 413 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 77; 2022: 79; 2023: 63; 2024: 70; 2025: 72; 2026: 51.

413 papers.

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  1. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 179
  2. 2021Graph Learning: A Surveycited by 141
  3. 2021MagNet: A Neural Network for Directed Graphscited by 107
  4. 2023Specformer: Spectral Graph Neural Networks Meet Transformerscited by 102
  5. 2021Video Summarization Using Deep Neural Networks: A Surveycited by 85
  6. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 82
  7. 2022Benchmarking and Analyzing Point Cloud Classification under Corruptionscited by 70
  8. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  9. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
  10. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  11. 2021Transferability Properties of Graph Neural Networkscited by 43
  12. 2022Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrumcited by 43
38 more
  1. 2021Simplicial Convolutional Neural Networkscited by 40
  2. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  3. 2021Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Studycited by 36
  4. 2024Spectrally Pruned Gaussian Fields with Neural Compensationcited by 33
  5. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  6. 2021Interpretable Stability Bounds for Spectral Graph Filterscited by 28
  7. 2021Moving Object Detection for Event-based vision using Graph Spectral Clusteringcited by 28
  8. 2022RU-Net: Regularized Unrolling Network for Scene Graph Generationcited by 28
  9. 2021Learning to Learn Graph Topologiescited by 26
  10. 2023A Survey on Spectral Graph Neural Networkscited by 26
  11. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  12. 2021Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Gridscited by 24
  13. 2021Spatio-temporal graph neural networks for multi-site PV power forecastingcited by 24
  14. 2022Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approachcited by 23
  15. 2022Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Predictioncited by 22
  16. 2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
  17. 2021Training Robust Graph Neural Networks with Topology Adaptive Edge Droppingcited by 20
  18. 2022coVariance Neural Networkscited by 20
  19. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  20. 2023Explainable Brain Age Prediction using coVariance Neural Networkscited by 20
  21. 2021A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinationscited by 18
  22. 2021Stability of Graph Convolutional Neural Networks to Stochastic Perturbationscited by 18
  23. 2022Simplicial Convolutional Filterscited by 18
  24. 2022PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsamplingcited by 18
  25. 2022Simplified Graph Convolution with Heterophilycited by 17
  26. 2022Node-oriented Spectral Filtering for Graph Neural Networkscited by 17
  27. 2021Accelerated Graph Learning from Smooth Signalscited by 16
  28. 2021Finite Impulse Response Filters for Simplicial Complexescited by 15
  29. 2021Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networkscited by 15
  30. 2021Untrained Graph Neural Networks for Denoisingcited by 15
  31. 2022How Expressive are Transformers in Spectral Domain for Graphs?cited by 15
  32. 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classificationscited by 15
  33. 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphscited by 15
  34. 2021Wide and Deep Graph Neural Network with Distributed Online Learningcited by 14
  35. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 14
  36. 2021Graph Signal Restoration Using Nested Deep Algorithm Unrollingcited by 13
  37. 2022Homophily modulates double descent generalization in graph convolution networkscited by 13
  38. 2020Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learningcited by 12

50 most cited of 413 All 413 (JSON)