Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2037
Full metadata record
DC FieldValueLanguage
dc.contributor.authorÖztürk, K.-
dc.contributor.authorPolat, F.-
dc.contributor.authorÖzyer, Tansel-
dc.date.accessioned2019-07-10T14:42:47Z
dc.date.available2019-07-10T14:42:47Z
dc.date.issued2017-07-31
dc.identifier.citationOzturk, K., Polat, F., & Ozyer, T. (2017, July). An Evolutionary Approach for Detecting Communities in Social Networks. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 966-973). ACM.en_US
dc.identifier.isbn978-145034993-2
dc.identifier.urihttps://dl.acm.org/citation.cfm?doid=3110025.3110157-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2037-
dc.description9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2017 : Sydney; Australia)
dc.description.abstractRapid development and wide usage of social networking applications have enabled large amounts of valuable data which can be analyzed for various reasons by companies, governments, non-profit organizations such as UN. This paper presents an evolutionary approach for detecting communities in social networks. We formulated a genetic algorithm that does not require the number of communities as input and is able to detect communities effectively in a very fast way. The performance of the proposed method is compared to its counterparts in order to show that good results can be generated. Additionally, we have done experiments using Newman’s Spectral Clustering Method as a pre-processing step and it gave much better results. © 2017 Association for Computing Machinery.en_US
dc.description.sponsorshipACM SIGMOD,Gemalto,IEEE Computer Society,IEEE TCDE,Springer Nature
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery, Inc.en_US
dc.relation.ispartofProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Miningen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlgorithms en_US
dc.subject Population dynamics en_US
dc.subject detect communitiesen_US
dc.titleAn Evolutionary Approach for Detecting Communities in Social Networksen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage966
dc.identifier.endpage973
dc.identifier.scopus2-s2.0-85040230230en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1145/3110025.3110157-
dc.authorscopusid8914139000-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

3
checked on Dec 21, 2024

Page view(s)

90
checked on Dec 23, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.