# Optimal Junction Trees

arXiv: https://arxiv.org/abs/1302.6823 · HTML: https://mlsanity.com/paper/1302.6823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2013.

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

- [Neural Trees for Learning on Graphs](https://mlsanity.com/paper/2105.07264/) (2021)
- [Tree-Constrained Graph Neural Networks For Argument Mining](https://mlsanity.com/paper/2110.00124/) (2021)
- [A More General Theory of Diagnosis from First Principles](https://mlsanity.com/paper/2309.16180/) (2023)
- [A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables](https://mlsanity.com/paper/2605.10651/) (2026)

## Abstract (arXiv)

The paper deals with optimality issues in connection with updating beliefs in networks. We address two processes: triangulation and construction of junction trees. In the first part, we give a simple algorithm for constructing an optimal junction tree from a triangulated network. In the second part, we argue that any exact method based on local calculations must either be less efficient than the junction tree method, or it has an optimality problem equivalent to that of triangulation.