Authors compute new small two-color ordered and cyclic Ramsey numbers for monotone paths, cycles, stars, complete graphs and nested matchings via SAT solving, determine closed forms for several pairs of graph classes, obtain bounds, apply reinforcement learning for lower bounds, and introduce permut
Brouwer and Willem H
4 Pith papers cite this work. Polarity classification is still indexing.
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RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
The spectrum of the unit-graph on Mat_3(F_q) consists of four distinct eigenvalues, implying a spectral gap that ensures large subsets have invertible differences.
Introduces clique graphs on graphs with unique ω-clique edge covers and derives spectral bounds, strongly regular classifications, and applications to existence questions.
citing papers explorer
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Some results on small ordered and cyclic Ramsey numbers
Authors compute new small two-color ordered and cyclic Ramsey numbers for monotone paths, cycles, stars, complete graphs and nested matchings via SAT solving, determine closed forms for several pairs of graph classes, obtain bounds, apply reinforcement learning for lower bounds, and introduce permut
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RLGT: A reinforcement learning framework for extremal graph theory
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
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Spectrum of the Unit-Graph on $\mathrm{Mat}_3(\mathbb{F}_q)$
The spectrum of the unit-graph on Mat_3(F_q) consists of four distinct eigenvalues, implying a spectral gap that ensures large subsets have invertible differences.
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A Comprehensive Study of Clique Graphs and Clique Regular Graphs
Introduces clique graphs on graphs with unique ω-clique edge covers and derives spectral bounds, strongly regular classifications, and applications to existence questions.