Graph Coloring Using Heat Diffusion
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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.
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.
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References · 8
- 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 269
- 2018Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Searchcited by 183
- 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 108
- 2019Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problemscited by 40
- 2022The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insightscited by 39
- 2017Traffic flow optimization using a quantum annealercited by 29
- 2020It's Not What Machines Can Learn, It's What We Cannot Teachcited by 29
- 2024Efficient Combinatorial Optimization via Heat Diffusioncited by 2
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