{"title":"社区检测使用快速余弦共享链路方法","authors":"Laxmi Chaudhary, Buddha Singh","doi":"10.1109/IADCC.2018.8692102","DOIUrl":null,"url":null,"abstract":"Finding communities in a complex network is tedious task. In this paper, we have proposed a Fast Cosine Shared Link (FCSL) method for unveiling and analyzing concealed behavior of the communities in the network. We have used Cosine similarity measure to find the node’s similarity. Further, we have evaluated the time taken to identify the communities in the network. Substantial experiments and results shows the potential of the proposed method to successfully find real world communities in real world network datasets. The experiments we carried out exhibit that our method outperforms other techniques and slightly improve results of the other existing methods, proving reliable results. The performance of methods evaluated in terms of communities, modularity value and time taken to detect the communities in network.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Community Detection using Fast Cosine Shared Link Method\",\"authors\":\"Laxmi Chaudhary, Buddha Singh\",\"doi\":\"10.1109/IADCC.2018.8692102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Finding communities in a complex network is tedious task. In this paper, we have proposed a Fast Cosine Shared Link (FCSL) method for unveiling and analyzing concealed behavior of the communities in the network. We have used Cosine similarity measure to find the node’s similarity. Further, we have evaluated the time taken to identify the communities in the network. Substantial experiments and results shows the potential of the proposed method to successfully find real world communities in real world network datasets. The experiments we carried out exhibit that our method outperforms other techniques and slightly improve results of the other existing methods, proving reliable results. The performance of methods evaluated in terms of communities, modularity value and time taken to detect the communities in network.\",\"PeriodicalId\":365713,\"journal\":{\"name\":\"2018 IEEE 8th International Advance Computing Conference (IACC)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 8th International Advance Computing Conference (IACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IADCC.2018.8692102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 8th International Advance Computing Conference (IACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IADCC.2018.8692102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Community Detection using Fast Cosine Shared Link Method
Finding communities in a complex network is tedious task. In this paper, we have proposed a Fast Cosine Shared Link (FCSL) method for unveiling and analyzing concealed behavior of the communities in the network. We have used Cosine similarity measure to find the node’s similarity. Further, we have evaluated the time taken to identify the communities in the network. Substantial experiments and results shows the potential of the proposed method to successfully find real world communities in real world network datasets. The experiments we carried out exhibit that our method outperforms other techniques and slightly improve results of the other existing methods, proving reliable results. The performance of methods evaluated in terms of communities, modularity value and time taken to detect the communities in network.