{"title":"基于拓扑的社区中节点属性相似性的研究。","authors":"Rajesh Sharma, D. Montesi","doi":"10.1145/3184558.3191564","DOIUrl":null,"url":null,"abstract":"One of the important problems in the domain of network science is the community detection. In the past, various topological based community detection algorithms have been proposed. Recently, researchers have taken into account at- tributes of the nodes while proposing community detection algorithms. In this work, we investigate if the nodes in a community, identified through topology based algorithms al- so exhibit attribute similarity. Using four different kinds of similarity metrics, we analyse the attribute similarity of the nodes within the communities derived using five different types of topological based community detection algorithms. Based on our analysis of three real social network datasets, we found on an average of 50% attribute similarity among the nodes in the communities.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Investigating Similarity of Nodes' Attributes in Topological Based Communities.\",\"authors\":\"Rajesh Sharma, D. Montesi\",\"doi\":\"10.1145/3184558.3191564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the important problems in the domain of network science is the community detection. In the past, various topological based community detection algorithms have been proposed. Recently, researchers have taken into account at- tributes of the nodes while proposing community detection algorithms. In this work, we investigate if the nodes in a community, identified through topology based algorithms al- so exhibit attribute similarity. Using four different kinds of similarity metrics, we analyse the attribute similarity of the nodes within the communities derived using five different types of topological based community detection algorithms. Based on our analysis of three real social network datasets, we found on an average of 50% attribute similarity among the nodes in the communities.\",\"PeriodicalId\":235572,\"journal\":{\"name\":\"Companion Proceedings of the The Web Conference 2018\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion Proceedings of the The Web Conference 2018\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3184558.3191564\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion Proceedings of the The Web Conference 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3184558.3191564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating Similarity of Nodes' Attributes in Topological Based Communities.
One of the important problems in the domain of network science is the community detection. In the past, various topological based community detection algorithms have been proposed. Recently, researchers have taken into account at- tributes of the nodes while proposing community detection algorithms. In this work, we investigate if the nodes in a community, identified through topology based algorithms al- so exhibit attribute similarity. Using four different kinds of similarity metrics, we analyse the attribute similarity of the nodes within the communities derived using five different types of topological based community detection algorithms. Based on our analysis of three real social network datasets, we found on an average of 50% attribute similarity among the nodes in the communities.