{"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}
引用次数: 1
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.