{"title":"特定于发送方和接收方的块模型","authors":"Zhi Geng, Krzysztof Nowicki","doi":"10.21307/JOSS-2019-015","DOIUrl":null,"url":null,"abstract":"Abstract We propose a sender-specific blockmodel for network data which utilizes both the group membership and the identities of the vertices. This is accomplished by introducing the edge probabilities (ŵ¿,ν) for 1 ≤ i ≤ c, 1 ≤ v ≤ n, where í specifies the group membership of a sending vertex and ν specifies the identity of the receiving vertex. In addition, group membership is consider to be random, with parameters (í>í)í=io We present methods based on the EM algorithm for the parameter estimations and discuss the recovery of latent group memberships. A companion model, the receiver-specific blockmodel, is also introduced in which the edge probabilities (≠uj) for 1 ≤ u ≤ n, 1 < j < c depend on the membership of a vertex receiving a directed edge. We apply both models to several sets of social network data.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"16 1","pages":"1 - 34"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sender- and receiver-specific blockmodels\",\"authors\":\"Zhi Geng, Krzysztof Nowicki\",\"doi\":\"10.21307/JOSS-2019-015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We propose a sender-specific blockmodel for network data which utilizes both the group membership and the identities of the vertices. This is accomplished by introducing the edge probabilities (ŵ¿,ν) for 1 ≤ i ≤ c, 1 ≤ v ≤ n, where í specifies the group membership of a sending vertex and ν specifies the identity of the receiving vertex. In addition, group membership is consider to be random, with parameters (í>í)í=io We present methods based on the EM algorithm for the parameter estimations and discuss the recovery of latent group memberships. A companion model, the receiver-specific blockmodel, is also introduced in which the edge probabilities (≠uj) for 1 ≤ u ≤ n, 1 < j < c depend on the membership of a vertex receiving a directed edge. We apply both models to several sets of social network data.\",\"PeriodicalId\":35236,\"journal\":{\"name\":\"Journal of Social Structure\",\"volume\":\"16 1\",\"pages\":\"1 - 34\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Social Structure\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21307/JOSS-2019-015\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Social Structure","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21307/JOSS-2019-015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
Abstract We propose a sender-specific blockmodel for network data which utilizes both the group membership and the identities of the vertices. This is accomplished by introducing the edge probabilities (ŵ¿,ν) for 1 ≤ i ≤ c, 1 ≤ v ≤ n, where í specifies the group membership of a sending vertex and ν specifies the identity of the receiving vertex. In addition, group membership is consider to be random, with parameters (í>í)í=io We present methods based on the EM algorithm for the parameter estimations and discuss the recovery of latent group memberships. A companion model, the receiver-specific blockmodel, is also introduced in which the edge probabilities (≠uj) for 1 ≤ u ≤ n, 1 < j < c depend on the membership of a vertex receiving a directed edge. We apply both models to several sets of social network data.