mlsanityData to 7 Oct 2026

Graph Signal Processing: History, Development, Impact, and Outlook

arXiv 2303.12211 ↗First posted 21 Mar 2023eess.SPcited by 39

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Abstract (arXiv)

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains. In this article, we provide an overview of the evolution of GSP, from its origins to the challenges ahead. The first half is devoted to reviewing the history of GSP and explaining how it gave rise to an encompassing framework that shares multiple similarities with SP. A key message is that GSP has been critical to develop novel and technically sound tools, theory, and algorithms that, by leveraging analogies with and the insights of digital SP, provide new ways to analyze, process, and learn from graph signals. In the second half, we shift focus to review the impact of GSP on other disciplines. First, we look at the use of GSP in data science problems, including graph learning and graph-based deep learning. Second, we discuss the impact of GSP on applications, including neuroscience and image and video processing. We conclude with a brief discussion of the emerging and future directions of GSP.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Mar 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 11; 2025: 14; 2026: 9.

39 papers.

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  1. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  2. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  3. 2024Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matchingcited by 8
  4. 2024Continuous Product Graph Neural Networkscited by 8
  5. 2024Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videoscited by 7
  6. 2025Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairscited by 6
  7. 2023Network Topology Inference with Sparsity and Laplacian Constraintscited by 4
  8. 2024Binarized Simplicial Convolutional Neural Networkscited by 3
  9. 2024Correlating Time Series with Interpretable Convolutional Kernelscited by 3
  10. 2024Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learningcited by 3
  11. 2025Learning the Structure of Connection Graphscited by 3
  12. 2025Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspectivecited by 2
27 more
  1. 2025Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoringcited by 2
  2. 2024Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networkscited by 1
  3. 2024Graph Signal Adaptive Message Passingcited by 1
  4. 2025Deep spatio-temporal point processes: Advances and new directionscited by 1
  5. 2025Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learningcited by 1
  6. 2025Directed Acyclic Graph Convolutional Networkscited by 1
  7. 2025Learning Time-Varying Graph Signals via Koopmancited by 1
  8. 2026Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphscited by 1
  9. 2023Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signalscited by 0
  10. 2023Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformationcited by 0
  11. 2023Diffusion Maps for Signal Filtering in Graph Learningcited by 0
  12. 2024Fast Wrong-way Cycling Detection in CCTV Videos: Sparse Sampling is All You Needcited by 0
  13. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0
  14. 2025Reconstruction of Graph Signals on Complex Manifolds with Kernel Methodscited by 0
  15. 2025Unrolling Dynamic Programming via Graph Filterscited by 0
  16. 2025Second-Order Tensorial Partial Differential Equations on Graphscited by 0
  17. 2025From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walkscited by 0
  18. 2025Learning Time-Varying Graphs from Incomplete Graph Signalscited by 0
  19. 2025Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filteringcited by 0
  20. 2026SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEGcited by 0
  21. 2026Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Modelscited by 0
  22. 2026Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphscited by 0
  23. 2026Topological Signal Processing: An Application-Oriented Tutorialcited by 0
  24. 2026What is Smoothness?cited by 0
  25. 2026Graph Learning with Spectral Connectivity Priors for Scarce Datacited by 0
  26. 2026Hierarchical Clustering and Signal Denoising on Digraphscited by 0
  27. 2026Signal Processing over Product DAGs: Causal Shifts and Filterscited by 0