{"title":"Novel Clique enumeration heuristic for detecting overlapping clusters","authors":"R. Schmitt, P. Ramos, Rafael de Santiago, L. Lamb","doi":"10.1109/CEC.2017.7969466","DOIUrl":null,"url":null,"abstract":"There are several known methods for detecting overlapping communities in graphs, each one having their advantages and limitations. The Clique Percolation Method (CPM) is one such method. CPM works by joining highly connected subgraphs (cliques) and using it to find the graph communities. However, the clique enumeration problem is NP-Hard, taking exponential time to be solved. This makes its use impractical in large real-world networks and applications. The aim of this paper is to present an efficient heuristic to enumerate cliques. This enables the Clique Percolation Method to detect overlapping communities in networks containing thousands of nodes. The analyses showed that our novel heuristic is competitive with other known methods regarding solution quality and we also make the CPM more scalable.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2017.7969466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
There are several known methods for detecting overlapping communities in graphs, each one having their advantages and limitations. The Clique Percolation Method (CPM) is one such method. CPM works by joining highly connected subgraphs (cliques) and using it to find the graph communities. However, the clique enumeration problem is NP-Hard, taking exponential time to be solved. This makes its use impractical in large real-world networks and applications. The aim of this paper is to present an efficient heuristic to enumerate cliques. This enables the Clique Percolation Method to detect overlapping communities in networks containing thousands of nodes. The analyses showed that our novel heuristic is competitive with other known methods regarding solution quality and we also make the CPM more scalable.
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