# Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

arXiv: https://arxiv.org/abs/2102.02431 · HTML: https://mlsanity.com/paper/2102.02431/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Network Inference via the Time-Varying Graphical Lasso](https://mlsanity.com/paper/1703.01958/) (2017)
- [Learning Graphical Models With Hubs](https://mlsanity.com/paper/1402.7349/) (2014)
- [A computationally fast alternative to cross-validation in penalized Gaussian graphical models](https://mlsanity.com/paper/1309.6216/) (2013)
- [Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners](https://mlsanity.com/paper/1404.1240/) (2014)
- [Contrastive Structured Anomaly Detection for Gaussian Graphical Models](https://mlsanity.com/paper/1605.00355/) (2016)

## Built on by (most cited first, 2 of 2)

- [Out-of-Distribution Detection using BiGAN and MDL](https://mlsanity.com/paper/2206.01851/) (2022)
- [Out-of-Distribution Detection using Maximum Entropy Coding](https://mlsanity.com/paper/2404.17023/) (2024)

## Abstract (arXiv)

A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for data analysis beyond simple model selection and sequences of scalars. More specifically, we extend the description length for data analysis in Gaussian graphical models. These are powerful tools to model interactions among variables in a sequence of i.i.d Gaussian data in the form of a graph. Our method uses universal graph coding methods to accurately account for model complexity, and therefore provide a more rigorous approach for graph model selection. The developed method is tested with synthetic and electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian graphical models. The experiments show that our method gives better performance compared to commonly used methods.