mlsanityData to 7 Oct 2026

Joint Gaussian Graphical Model Estimation: A Survey

arXiv 2110.10281 ↗First posted 19 Oct 2021stat.ME cs.LG stat.MLcited by 10

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Abstract (arXiv)

Graphs from complex systems often share a partial underlying structure across domains while retaining individual features. Thus, identifying common structures can shed light on the underlying signal, for instance, when applied to scientific discoveries or clinical diagnoses. Furthermore, growing evidence shows that the shared structure across domains boosts the estimation power of graphs, particularly for high-dimensional data. However, building a joint estimator to extract the common structure may be more complicated than it seems, most often due to data heterogeneity across sources. This manuscript surveys recent work on statistical inference of joint Gaussian graphical models, identifying model structures that fit various data generation processes. Simulations under different data generation processes are implemented with detailed discussions on the choice of models.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 36 of its 108 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

36 of 108 references.

References · 36

  1. 2016Structure Learning in Graphical Modelingcited by 66
  2. 2017Network Inference via the Time-Varying Graphical Lassocited by 55
  3. 2013Node-Based Learning of Multiple Gaussian Graphical Modelscited by 26
  4. 2015The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Functioncited by 17
  5. 2013Asymptotic normality and optimalities in estimation of large Gaussian graphical modelscited by 12
  6. 2018High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Modelscited by 12
  7. 2013Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Seriescited by 11
  8. 2019Integrative Factor Regression and Its Inference for Multimodal Data Analysiscited by 11
  9. 2015Differential Network Analysis via the Lasso Penalized D-Trace Losscited by 10
  10. 2016Simultaneous Clustering and Estimation of Heterogeneous Graphical Modelscited by 10
  11. 2018Estimating Time-Varying Graphical Modelscited by 8
  12. 2019Direct Estimation of Differential Functional Graphical Modelscited by 8
24 more
  1. 2013Joint Estimation of Multiple Graphical Models from High Dimensional Time Seriescited by 7
  2. 2014Confidence intervals for high-dimensional inverse covariance estimationcited by 7
  3. 2017Time-dependent spatially varying graphical models, with application to brain fMRI data analysiscited by 7
  4. 2019Two-sample inference for high-dimensional Markov networkscited by 7
  5. 2019Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivitycited by 7
  6. 2020Differential Network Analysis: A Statistical Perspectivecited by 7
  7. 2015Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lassocited by 6
  8. 2015Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivitycited by 6
  9. 2017Bayesian Joint Modeling of Multiple Brain Functional Networkscited by 6
  10. 2015ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Modelscited by 5
  11. 2016Joint Estimation of Precision Matrices in Heterogeneous Populationscited by 5
  12. 2019Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matchingcited by 5
  13. 2020Statistical Inference for Networks of High-Dimensional Point Processescited by 5
  14. 2014Inference for Sparse Conditional Precision Matricescited by 3
  15. 2015On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Datacited by 3
  16. 2016Bayesian inference for multiple Gaussian graphical models with application to metabolic association networkscited by 3
  17. 2020Estimating Multiple Precision Matrices with Cluster Fusion Regularizationcited by 3
  18. 2015Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classescited by 2
  19. 2018Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Modelscited by 2
  20. 2018Bayesian Joint Spike-and-Slab Graphical Lassocited by 2
  21. 2021Direct estimation of differential Granger causality between two high-dimensional time seriescited by 2
  22. 2014Joint Structure Learning of Multiple Non-Exchangeable Networkscited by 1
  23. 2019Bayesian Inference of Networks Across Multiple Sample Groups and Data Typescited by 1
  24. 2021Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processescited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 5; 2025: 1.

10 papers.

  1. 2022Joint Network Topology Inference via a Shared Graphon Modelcited by 11
  2. 2022Latent Multimodal Functional Graphical Model Estimationcited by 3
  3. 2023Learning High-Dimensional Differential Graphs From Multi-Attribute Datacited by 3
  4. 2024Multiview Graph Learning with Consensus Graphcited by 3
  5. 2024Estimation of partially known Gaussian graphical models with score-based structural priorscited by 2
  6. 2024Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimationcited by 2
  7. 2024Spectral Differential Network Analysis for High-Dimensional Time Seriescited by 1
  8. 2023Exploration of the search space of Gaussian graphical models for paired datacited by 0
  9. 2024Learning Networks from Wide-Sense Stationary Stochastic Processescited by 0
  10. 2025Co-Hub Node Based Multiview Graph Learning with Theoretical Guaranteescited by 0