mlsanityData to 13 Oct 2026

Accelerated Graph Learning from Smooth Signals

arXiv 2110.09677 ↗First posted 19 Oct 2021cs.LG eess.SPcited by 16

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Abstract (arXiv)

We consider network topology identification subject to a signal smoothness prior on the nodal observations. A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions. Unlike existing solvers, the novel iterations come with global convergence rate guarantees and do not require additional step-size tuning. Reproducible simulated tests demonstrate the effectiveness of the proposed method in accurately recovering random and real-world graphs, markedly faster than state-of-the-art alternatives and without incurring an extra computational burden.

Key facts. Cited by 16 papers in the map (data through 13 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

14 of 41 references.

References · 14

  1. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 411
  2. 2016How to learn a graph from smooth signalscited by 146
  3. 2018Learning graphs from data: A signal representation perspectivecited by 119
  4. 2018Connecting the Dots: Identifying Network Structure via Graph Signal Processingcited by 111
  5. 2016Network Topology Inference from Spectral Templatescited by 58
  6. 2019A Unified Framework for Structured Graph Learning via Spectral Constraintscited by 53
  7. 2020Signal Processing on Directed Graphscited by 28
  8. 2017Large Scale Graph Learning from Smooth Signalscited by 27
  9. 2016Learning Sparse Graphs Under Smoothness Priorcited by 23
  10. 2020Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Informationcited by 19
  11. 2017Learning Graphs with Monotone Topology Properties and Multiple Connected Componentscited by 13
  12. 2018Graph topology inference based on sparsifying transform learningcited by 10
2 more
  1. 2021Online Graph Learning under Smoothness Priorscited by 8
  2. 2016Network Topology Identification from Spectral Templatescited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 4; 2026: 3.

16 papers.

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  1. 2024Non-negative Weighted DAG Structure Learningcited by 7
  2. 2022Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signalscited by 4
  3. 2024Graph Structure Learning with Interpretable Bayesian Neural Networkscited by 4
  4. 2022Joint graph learning from Gaussian observations in the presence of hidden nodescited by 3
  5. 2023Graph Laplacian Learning with Exponential Family Noisecited by 3
  6. 2023A Unified Framework for Fair Spectral Clustering With Effective Graph Learningcited by 3
  7. 2023Learning to Identify Graphs from Node Trajectories in Multi-Robot Networkscited by 1
  8. 2023Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signalscited by 0
  9. 2024Online Proximal ADMM for Graph Learning from Streaming Smooth Signalscited by 0
  10. 2025Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detectioncited by 0
  11. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0
  12. 2025Non-negative DAG Learning from Time-Series Datacited by 0
4 more
  1. 2025Topology Identification and Inference over Graphscited by 0
  2. 2026Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularizationcited by 0
  3. 2026Directed Graph Topology Inference via Graph Filter Identificationcited by 0
  4. 2026Multi-Source Wasserstein Distributionally Robust Graph Learningcited by 0