{"title":"DENGRAPH:基于密度的社区检测算法","authors":"Tanja Falkowski, Anja Barth, M. Spiliopoulou","doi":"10.1109/WI.2007.43","DOIUrl":null,"url":null,"abstract":"Detecting densely connected subgroups in graphs such as communities in social networks is of interest in many research fields. Several methods have been developed to find communities but most of them have a high time complexity and are thus not applicable for large networks. Inspired by the clustering algorithm incremental DBSCAN we propose a density-based graph clustering algorithm DENGRAPH that is designed to deal with large dynamic datasets with noise and present first experimental results.","PeriodicalId":192501,"journal":{"name":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"92","resultStr":"{\"title\":\"DENGRAPH: A Density-based Community Detection Algorithm\",\"authors\":\"Tanja Falkowski, Anja Barth, M. Spiliopoulou\",\"doi\":\"10.1109/WI.2007.43\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detecting densely connected subgroups in graphs such as communities in social networks is of interest in many research fields. Several methods have been developed to find communities but most of them have a high time complexity and are thus not applicable for large networks. Inspired by the clustering algorithm incremental DBSCAN we propose a density-based graph clustering algorithm DENGRAPH that is designed to deal with large dynamic datasets with noise and present first experimental results.\",\"PeriodicalId\":192501,\"journal\":{\"name\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"92\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2007.43\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2007.43","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DENGRAPH: A Density-based Community Detection Algorithm
Detecting densely connected subgroups in graphs such as communities in social networks is of interest in many research fields. Several methods have been developed to find communities but most of them have a high time complexity and are thus not applicable for large networks. Inspired by the clustering algorithm incremental DBSCAN we propose a density-based graph clustering algorithm DENGRAPH that is designed to deal with large dynamic datasets with noise and present first experimental results.