Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach

Ali Kanso; Dragan Stevanovic; Mohammad Ghebleh; Salem Al-Yakoob

Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach

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arxiv 2403.18429 v2 pith:SJSDNUA2 submitted 2024-03-27 math.CO

Mohammad Ghebleh , Salem Al-Yakoob , Ali Kanso , Dragan Stevanovic This is my paper · ORCID

classification math.CO

keywords approachgraphlearningreinforcementtheorywagneradamapplies

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We reimplement here the recent approach of Adam Zsolt Wagner [arXiv:2104.14516], which applies reinforcement learning to construct (counter)examples in graph theory, in order to make it more readable, more stable and much faster. The presented concepts are illustrated by constructing counterexamples for a number of published conjectured bounds for the Laplacian spectral radius of graphs.

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Reinforcement learning for graph theory, Parallelizing Wagner's approach
math.CO 2025-09 conditional novelty 4.0 of 10

A parallelized RL search over graphs produces three new counterexamples to conjectured Laplacian spectral radius bounds, alongside a speedup claim that is only weakly supported.

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