Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Theoretical computer science 2016-09, Vol.647, p.101-111
Main Authors: Vignatti, André L., da Silva, Murilo V.G.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3
cites cdi_FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3
container_end_page 111
container_issue
container_start_page 101
container_title Theoretical computer science
container_volume 647
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
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1835594877</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0304397516303899</els_id><sourcerecordid>1835594877</sourcerecordid><originalsourceid>FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3</originalsourceid><addsrcrecordid>eNp9kDtPAzEQhC0EEiHwA-hc0txhn-_OtqhQxEsKoklvOfZe4uhe2L4E-PU4CjXb7BQzq50PoVtKckpofb_Lowl5kWRORE5IcYZmVHCZFYUsz9GMMFJmTPLqEl2FsCNpKl7P0Ord9a6bOrwHH-ELmyEJ7Hq8gR68bt0PWOx1b4cOb7wetwEfXNzicTgkX6sP2MLGA2DrQvRuPUU39NfootFtgJu_PUer56fV4jVbfry8LR6XmWGcxQxoVZeEE0hvF1w2vGaMWsoolVALWsi1gGZttRGScSIMB1M1hFkoOZEU2Bzdnc6OfvicIETVuWCgbXUPwxQUFayqZCk4T1Z6sho_hOChUaN3nfbfihJ1BKh2KgFUR4CKCJUApszDKQOpwt6BV8E46A1Y58FEZQf3T_oXqsR5IQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1835594877</pqid></control><display><type>article</type><title>Minimum vertex cover in generalized random graphs with power law degree distribution</title><source>ScienceDirect Journals</source><creator>Vignatti, André L. ; da Silva, Murilo V.G.</creator><creatorcontrib>Vignatti, André L. ; da Silva, Murilo V.G.</creatorcontrib><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&gt;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).</description><identifier>ISSN: 0304-3975</identifier><identifier>EISSN: 1879-2294</identifier><identifier>DOI: 10.1016/j.tcs.2016.08.002</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>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</subject><ispartof>Theoretical computer science, 2016-09, Vol.647, p.101-111</ispartof><rights>2016 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3</citedby><cites>FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Vignatti, André L.</creatorcontrib><creatorcontrib>da Silva, Murilo V.G.</creatorcontrib><title>Minimum vertex cover in generalized random graphs with power law degree distribution</title><title>Theoretical computer science</title><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&gt;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).</description><subject>Approximation</subject><subject>Approximation algorithms</subject><subject>Britton random graph model</subject><subject>Chung–Lu random graph model</subject><subject>Computer simulation</subject><subject>Constants</subject><subject>Generalized random graph model</subject><subject>Graphs</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Power law</subject><subject>Power-law graphs</subject><subject>Vertex cover problem</subject><issn>0304-3975</issn><issn>1879-2294</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kDtPAzEQhC0EEiHwA-hc0txhn-_OtqhQxEsKoklvOfZe4uhe2L4E-PU4CjXb7BQzq50PoVtKckpofb_Lowl5kWRORE5IcYZmVHCZFYUsz9GMMFJmTPLqEl2FsCNpKl7P0Ord9a6bOrwHH-ELmyEJ7Hq8gR68bt0PWOx1b4cOb7wetwEfXNzicTgkX6sP2MLGA2DrQvRuPUU39NfootFtgJu_PUer56fV4jVbfry8LR6XmWGcxQxoVZeEE0hvF1w2vGaMWsoolVALWsi1gGZttRGScSIMB1M1hFkoOZEU2Bzdnc6OfvicIETVuWCgbXUPwxQUFayqZCk4T1Z6sho_hOChUaN3nfbfihJ1BKh2KgFUR4CKCJUApszDKQOpwt6BV8E46A1Y58FEZQf3T_oXqsR5IQ</recordid><startdate>20160927</startdate><enddate>20160927</enddate><creator>Vignatti, André L.</creator><creator>da Silva, Murilo V.G.</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20160927</creationdate><title>Minimum vertex cover in generalized random graphs with power law degree distribution</title><author>Vignatti, André L. ; da Silva, Murilo V.G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Approximation</topic><topic>Approximation algorithms</topic><topic>Britton random graph model</topic><topic>Chung–Lu random graph model</topic><topic>Computer simulation</topic><topic>Constants</topic><topic>Generalized random graph model</topic><topic>Graphs</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Power law</topic><topic>Power-law graphs</topic><topic>Vertex cover problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vignatti, André L.</creatorcontrib><creatorcontrib>da Silva, Murilo V.G.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Theoretical computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vignatti, André L.</au><au>da Silva, Murilo V.G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Minimum vertex cover in generalized random graphs with power law degree distribution</atitle><jtitle>Theoretical computer science</jtitle><date>2016-09-27</date><risdate>2016</risdate><volume>647</volume><spage>101</spage><epage>111</epage><pages>101-111</pages><issn>0304-3975</issn><eissn>1879-2294</eissn><abstract>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&gt;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).</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.tcs.2016.08.002</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0304-3975
ispartof Theoretical computer science, 2016-09, Vol.647, p.101-111
issn 0304-3975
1879-2294
language eng
recordid cdi_proquest_miscellaneous_1835594877
source ScienceDirect Journals
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T10%3A59%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Minimum%20vertex%20cover%20in%20generalized%20random%20graphs%20with%20power%20law%20degree%20distribution&rft.jtitle=Theoretical%20computer%20science&rft.au=Vignatti,%20Andr%C3%A9%20L.&rft.date=2016-09-27&rft.volume=647&rft.spage=101&rft.epage=111&rft.pages=101-111&rft.issn=0304-3975&rft.eissn=1879-2294&rft_id=info:doi/10.1016/j.tcs.2016.08.002&rft_dat=%3Cproquest_cross%3E1835594877%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c373t-e1564070e201279f76331d13119e68129b8efbdac893708c7ec5f03de47091e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1835594877&rft_id=info:pmid/&rfr_iscdi=true