{"title":"检测动态和静态地理社会社区","authors":"Frank Lin, R. Renner","doi":"10.1109/COMGEO.2013.24","DOIUrl":null,"url":null,"abstract":"How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and LabelRankT community detection algorithm in Gephi, a graph visualization tool. We researched extending these community detection algorithms to incorporate the geospatial distance between nodes in a network as a limiting factor for the automatic detection of community formation.","PeriodicalId":383309,"journal":{"name":"2013 Fourth International Conference on Computing for Geospatial Research and Application","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting Dynamic and Static Geo-social Communities\",\"authors\":\"Frank Lin, R. Renner\",\"doi\":\"10.1109/COMGEO.2013.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and LabelRankT community detection algorithm in Gephi, a graph visualization tool. We researched extending these community detection algorithms to incorporate the geospatial distance between nodes in a network as a limiting factor for the automatic detection of community formation.\",\"PeriodicalId\":383309,\"journal\":{\"name\":\"2013 Fourth International Conference on Computing for Geospatial Research and Application\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Fourth International Conference on Computing for Geospatial Research and Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMGEO.2013.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth International Conference on Computing for Geospatial Research and Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMGEO.2013.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting Dynamic and Static Geo-social Communities
How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and LabelRankT community detection algorithm in Gephi, a graph visualization tool. We researched extending these community detection algorithms to incorporate the geospatial distance between nodes in a network as a limiting factor for the automatic detection of community formation.