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https://dx.doi.org/10.48550/ar...
Article . 2018
License: arXiv Non-Exclusive Distribution
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Counting maximal near perfect matchings in quasirandom and dense graphs

Authors: Yifan Jing; Akbar Rafiey;

Counting maximal near perfect matchings in quasirandom and dense graphs

Abstract

A maximal $\varepsilon$-near perfect matching is a maximal matching which covers at least $(1-\varepsilon)|V(G)|$ vertices. In this paper, we study the number of maximal near perfect matchings in generalized quasirandom and dense graphs. We provide tight lower and upper bounds on the number of $\varepsilon$-near perfect matchings in generalized quasirandom graphs. Moreover, based on these results, we provide a deterministic polynomial time algorithm that for a given dense graph $G$ of order $n$ and a real number $\varepsilon>0$, returns either a conclusion that $G$ has no $\varepsilon$-near perfect matching, or a positive non-trivial number $\ell$ such that the number of maximal $\varepsilon$-near perfect matchings in $G$ is at least $n^{\ell n}$. Our algorithm uses algorithmic version of Szemer��di Regularity Lemma, and has $O(f(\varepsilon)n^{5/2})$ time complexity. Here $f(\cdot)$ is an explicit function depending only on $\varepsilon$.

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Keywords

FOS: Computer and information sciences, 05C70, 05C80, 05C85, Discrete Mathematics (cs.DM), G.2, FOS: Mathematics, Mathematics - Combinatorics, Combinatorics (math.CO), Computer Science - Discrete Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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