Graph Coloring: Comparing Cluster Graphs to Factor Graphs
Machine Learning
2021-10-06 v1
Abstract
We present a means of formulating and solving graph coloring problems with probabilistic graphical models. In contrast to the prevalent literature that uses factor graphs for this purpose, we instead approach it from a cluster graph perspective. Since there seems to be a lack of algorithms to automatically construct valid cluster graphs, we provide such an algorithm (termed LTRIP). Our experiments indicate a significant advantage for preferring cluster graphs over factor graphs, both in terms of accuracy as well as computational efficiency.
Cite
@article{arxiv.2110.02048,
title = {Graph Coloring: Comparing Cluster Graphs to Factor Graphs},
author = {Simon Streicher and Johan du Preez},
journal= {arXiv preprint arXiv:2110.02048},
year = {2021}
}