# The Gaussian Graphical Model in Cross-sectional and Time-series Data

arXiv: https://arxiv.org/abs/1609.04156 · HTML: https://mlsanity.com/paper/1609.04156/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2016.

## Built on by (most cited first, 9 of 9)

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- [Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting](https://mlsanity.com/paper/2403.19442/) (2024)
- [Exploration of the search space of Gaussian graphical models for paired data](https://mlsanity.com/paper/2303.05561/) (2023)
- [Structured Estimation of Heterogeneous Time Series](https://mlsanity.com/paper/2311.08658/) (2023)
- [Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?](https://mlsanity.com/paper/2508.10919/) (2025)
- [Emotionally Vulnerable Subtype of Internet Gaming Disorder: Measuring and Exploring the Pathology of Problematic Generative AI Use](https://mlsanity.com/paper/2510.06908/) (2025)

## Abstract (arXiv)

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in 3 kinds of psychological datasets: datasets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means---the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.