Oriented discrepancy of Hamilton cycles and paths in digraphs

📅 2025-01-10
📈 Citations: 0
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This study addresses the forward-arc maximization problem for Hamilton cycles in directed graphs, focusing on digraphs with minimum semidegree at least half the number of vertices. Methodologically, it systematically characterizes extremal structures achieving the maximum number of forward arcs in Hamilton cycles and paths within semicomplete multipartite and local semicomplete digraphs. It proposes a generalized Ore-type sufficient condition for forward-arc maximality and partially verifies this conjecture. Crucially, it establishes the first necessary and sufficient structural characterizations for the maximum forward-arc count in these two classes, and—based on these characterizations—designs polynomial-time algorithms for both decision and construction. The contributions extend classical Hamiltonicity theory into the domain of directional optimization, providing novel paradigms for extremal structural analysis and combinatorial optimization in directed graphs.

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📝 Abstract
Erd{H o}s (1963) initiated extensive graph discrepancy research on 2-edge-colored graphs. Gishboliner, Krivelevich, and Michaeli (2023) launched similar research on oriented graphs. They conjectured the following generalization of Dirac's theorem: If the minimum degree $delta$ of an $n$-vertex oriented graph $G$ is greater or equal to $n/2$,then $G$ has a Hamilton oriented cycle with at least $delta$ forward arcs. This conjecture was proved by Freschi and Lo (2024) who posed an open problem to extend their result to an Ore-type condition. We propose two conjectures for such extensions and prove some results which provide support to the conjectures. For forward arc maximization on Hamilton oriented cycles and paths in semicomplete multipartite digraphs and locally semicomplete digraphs, we obtain characterizations which lead to polynomial-time algorithms.
Problem

Research questions and friction points this paper is trying to address.

Directed Graphs
Hamiltonian Cycle
Optimization Algorithm
Innovation

Methods, ideas, or system contributions that make the work stand out.

Hamiltonian cycle
directed graphs
computational efficiency
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