# Graph Coloring Using Heat Diffusion

arXiv: https://arxiv.org/abs/2404.14457 · HTML: https://mlsanity.com/paper/2404.14457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem](https://mlsanity.com/paper/1906.01227/) (2019)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [Combinatorial Optimization with Physics-Inspired Graph Neural Networks](https://mlsanity.com/paper/2107.01188/) (2021)
- [Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems](https://mlsanity.com/paper/1903.04598/) (2019)
- [The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights](https://mlsanity.com/paper/2203.02433/) (2022)
- [Traffic flow optimization using a quantum annealer](https://mlsanity.com/paper/1708.01625/) (2017)
- [It's Not What Machines Can Learn, It's What We Cannot Teach](https://mlsanity.com/paper/2002.09398/) (2020)
- [Efficient Combinatorial Optimization via Heat Diffusion](https://mlsanity.com/paper/2403.08757/) (2024)

## Abstract (arXiv)

Graph coloring is a problem with varied applications in industry and science such as scheduling, resource allocation, and circuit design. The purpose of this paper is to establish if a new gradient based iterative solver framework known as heat diffusion can solve the graph coloring problem. We propose a solution to the graph coloring problem using the heat diffusion framework. We compare the solutions against popular methods and establish the competitiveness of heat diffusion method for the graph coloring problem.