{"title":"Adaptations of the k-Means Algorithm to Community Detection in Parallel Environments","authors":"András Bóta, Miklós Krész, Bogdán Zaválnij","doi":"10.1109/SYNASC.2015.54","DOIUrl":null,"url":null,"abstract":"In this paper we present preliminary results for a fast parallel adaptation of the well-known k-means clustering algorithm to graphs. We are going to use our method to detect communities in complex networks. For testing purposes we will use the graph generator of Lancichinetti et al., and we are going to compare our method with the OSLOM, CPM, and hub percolation overlapping community detection methods.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"45 1","pages":"299-302"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2015.54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper we present preliminary results for a fast parallel adaptation of the well-known k-means clustering algorithm to graphs. We are going to use our method to detect communities in complex networks. For testing purposes we will use the graph generator of Lancichinetti et al., and we are going to compare our method with the OSLOM, CPM, and hub percolation overlapping community detection methods.
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