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Minimum vertex cover in generalized random graphs with power law degree distribution
In this paper we study the approximability of the minimum vertex cover problem in power law graphs. In particular, we investigate the behavior of a standard 2-approximation algorithm together with a simple pre-processing step when the input is a random sample from a generalized random graph model wi...
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Published in: | Theoretical computer science 2016-09, Vol.647, p.101-111 |
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creator | Vignatti, André L. da Silva, Murilo V.G. |
description | In this paper we study the approximability of the minimum vertex cover problem in power law graphs. In particular, we investigate the behavior of a standard 2-approximation algorithm together with a simple pre-processing step when the input is a random sample from a generalized random graph model with power law degree distribution. More precisely, if the probability of a vertex of degree i to be present in the graph is ci−β, where β>2 and c is a normalizing constant, the expected approximation ratio is 1+ζ(β)−1Liβ(e−ρ(β)), where ζ(β) is the Riemann Zeta function of β, Li(β) is the polylogarithmic special function of β and ρ(β)=Liβ−2(1e)ζ(β−1). |
doi_str_mv | 10.1016/j.tcs.2016.08.002 |
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subjects | Approximation Approximation algorithms Britton random graph model Chung–Lu random graph model Computer simulation Constants Generalized random graph model Graphs Mathematical analysis Mathematical models Power law Power-law graphs Vertex cover problem |
title | Minimum vertex cover in generalized random graphs with power law degree distribution |
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