mlsanityData to 7 Oct 2026

Learning graphs from data: A signal representation perspective

arXiv 1806.00848 ↗First posted 3 Jun 2018cs.LG cs.SI stat.MLcited by 119

Not on the map. Why?

Abstract (arXiv)

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph inference methods, and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine learning algorithms for learning graphs from data.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 24; 2023: 16; 2024: 27; 2025: 17; 2026: 12.

119 papers.

Sort:

By area:

  1. 2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 70
  2. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
  3. 2021Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 20
  4. 2021Accelerated Graph Learning from Smooth Signalscited by 16
  5. 2022GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networkscited by 14
  6. 2024Benchmarking Positional Encodings for GNNs and Graph Transformerscited by 13
  7. 2021Learning Time-Varying Graphs from Online Datacited by 12
  8. 2024Gegenbauer Graph Neural Networks for Time-varying Signal Reconstructioncited by 12
  9. 2022Joint Network Topology Inference via a Shared Graphon Modelcited by 11
  10. 2022Restructuring Graph for Higher Homophily via Adaptive Spectral Clusteringcited by 9
  11. 2022Learning Hypergraphs From Signals With Dual Smoothness Priorcited by 9
  12. 2021Online Graph Learning under Smoothness Priorscited by 8
38 more
  1. 2021Temporal Graph Signal Decompositioncited by 8
  2. 2022Learning to Infer Structures of Network Gamescited by 8
  3. 2024Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signalscited by 8
  4. 2024Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priorscited by 8
  5. 2025Graph Concept Bottleneck Modelscited by 8
  6. 2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clientscited by 7
  7. 2023Bures-Wasserstein Means of Graphscited by 6
  8. 2024HGAttack: Transferable Heterogeneous Graph Adversarial Attackcited by 6
  9. 2021SGL: Spectral Graph Learning from Measurementscited by 5
  10. 2021Sparse Graph Learning Under Laplacian-Related Constraintscited by 5
  11. 2022Learning Graph Structure from Convolutional Mixturescited by 5
  12. 2022Learning Product Graphs from Spectral Templatescited by 5
  13. 2022ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregationcited by 5
  14. 2024How Does Message Passing Improve Collaborative Filtering?cited by 5
  15. 2021Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertaintycited by 4
  16. 2021Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistencycited by 4
  17. 2022Community-based anomaly detection using spectral graph filteringcited by 4
  18. 2022Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signalscited by 4
  19. 2022Learning Expanding Graphs for Signal Interpolationcited by 4
  20. 2022An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observabilitycited by 4
  21. 2022Scalable Multi-view Clustering with Graph Filteringcited by 4
  22. 2022Linear Combinatorial Semi-Bandit with Causally Related Rewardscited by 4
  23. 2023Network Topology Inference with Sparsity and Laplacian Constraintscited by 4
  24. 2024SAGMAN: Stability Analysis of Graph Neural Networks on the Manifoldscited by 4
  25. 2024Graph Structure Learning with Interpretable Bayesian Neural Networkscited by 4
  26. 2024Efficient Learning of Balanced Signed Graphs via Iterative Linear Programmingcited by 4
  27. 2021Robust Graph Learning Under Wasserstein Uncertaintycited by 3
  28. 2023Graph Laplacian Learning with Exponential Family Noisecited by 3
  29. 2023A Unified Framework for Fair Spectral Clustering With Effective Graph Learningcited by 3
  30. 2023Learning graphs and simplicial complexes from datacited by 3
  31. 2024Multiview Graph Learning with Consensus Graphcited by 3
  32. 2024Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multi-Agent Trajectory Predictioncited by 3
  33. 2025Covariance Density Neural Networkscited by 3
  34. 2021Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Datacited by 2
  35. 2021Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fieldscited by 2
  36. 2022Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platformscited by 2
  37. 2022Adaptive Estimation of Graphical Models under Total Positivitycited by 2
  38. 2024Optimizing k in kNN Graphs with Graph Learning Perspectivecited by 2

50 most cited of 119 All 119 (JSON)