# Join-Graph Propagation Algorithms

arXiv: https://arxiv.org/abs/1401.3489 · HTML: https://mlsanity.com/paper/1401.3489/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 10 of 10)

- [Graph Coloring: Comparing Cluster Graphs to Factor Graphs](https://mlsanity.com/paper/2110.02048/) (2021)
- [Estimating Causal Effects from Learned Causal Networks](https://mlsanity.com/paper/2408.14101/) (2024)
- [IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees](https://mlsanity.com/paper/2202.12003/) (2022)
- [Deep Dependency Networks for Multi-Label Classification](https://mlsanity.com/paper/2302.00633/) (2023)
- [Markov Conditions and Factorization in Logical Credal Networks](https://mlsanity.com/paper/2302.14146/) (2023)
- [IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function](https://mlsanity.com/paper/2304.06366/) (2023)
- [Approximate inference of marginals using the IBIA framework](https://mlsanity.com/paper/2306.00335/) (2023)
- [Optimal Clustering with Dependent Costs in Bayesian Networks](https://mlsanity.com/paper/2308.03970/) (2023)
- [Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models](https://mlsanity.com/paper/2402.15075/) (2024)
- [Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification](https://mlsanity.com/paper/2404.11667/) (2024)

## Abstract (arXiv)

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.