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dc.contributor.authorMa, Zongjie
dc.contributor.authorFan, Yi
dc.contributor.authorSu, Kaile
dc.contributor.authorLi, Chengqian
dc.contributor.authorSattar, Abdul
dc.contributor.editorBourbakis, N
dc.contributor.editorEsposito, A
dc.contributor.editorMali, A
dc.contributor.editorAlamaniotis, M
dc.date.accessioned2018-04-13T01:30:22Z
dc.date.available2018-04-13T01:30:22Z
dc.date.issued2016
dc.identifier.isbn9781509044597
dc.identifier.issn1082-3409
dc.identifier.doi10.1109/ICTAI.2016.0109
dc.identifier.urihttp://hdl.handle.net/10072/341290
dc.description.abstractThe problem of finding a minimum vertex cover (MinVC) in a graph is a prominent NP-hard problem of great importance in both theory and application. During recent decades, there has been much interest in finding optimal or near-optimal solutions to this problem. Many existing heuristic algorithms for MinVC are based on local search strategies. Recently, an algorithm called FastVC takes a first step towards solving the MinVC problem for large real-world graphs. However, FastVC may be trapped by local minima during the local search stage due to the lack of suitable diversification mechanisms. In this work, we design a new random walk strategy to help FastVC escape from local minima. Experiments conducted on a broad range of large real-world graphs show that our algorithm outperforms state-of-the-art algorithms on most classes of the benchmark and finds smaller vertex covers on a considerable portion of the graphs.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencename28th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
dc.relation.ispartofconferencetitle2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016)
dc.relation.ispartofdatefrom2016-11-06
dc.relation.ispartofdateto2016-11-08
dc.relation.ispartoflocationSan Jose, CA
dc.relation.ispartofpagefrom686
dc.relation.ispartofpagefrom5 pages
dc.relation.ispartofpageto690
dc.relation.ispartofpageto5 pages
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleRandom Walk in Large Real-World Graphs for Finding Smaller Vertex Cover
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorSu, Kaile
gro.griffith.authorFan, Yi
gro.griffith.authorMa, Zongjie


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