# LDPC codes: comparing cluster graphs to factor graphs

arXiv: https://arxiv.org/abs/2204.06350 · HTML: https://mlsanity.com/paper/2204.06350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Graph Coloring: Comparing Cluster Graphs to Factor Graphs](https://mlsanity.com/paper/2110.02048/) (2021)
- [Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm](https://mlsanity.com/paper/2110.00091/) (2021)

## Abstract (arXiv)

We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between random variables during inference, which are advantageous in terms of computational cost, convergence speed, and accuracy of marginal probabilities. This study investigates these benefits in the context of LDPC codes and shows that a cluster graph representation outperforms the traditional factor graph representation.