# The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

arXiv: https://arxiv.org/abs/1407.8088 · HTML: https://mlsanity.com/paper/1407.8088/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2014.

## Built on by (most cited first, 4 of 4)

- [NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge](https://mlsanity.com/paper/2109.04286/) (2021)
- [A Comprehensively Improved Hybrid Algorithm for Learning Bayesian Networks: Multiple Compound Memory Erasing](https://mlsanity.com/paper/2212.03103/) (2022)
- [Causal Discovery in Multivariate Time Series through Mutual Information Featurization](https://mlsanity.com/paper/2508.01848/) (2025)
- [Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship](https://mlsanity.com/paper/2602.03353/) (2026)

## Abstract (arXiv)

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.