Sender- and receiver-specific blockmodels

Q2 Social Sciences
Zhi Geng, Krzysztof Nowicki
{"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":null,"pages":null},"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.
特定于发送方和接收方的块模型
摘要提出了一种针对网络数据发送方的块模型,该模型既利用了组成员关系,又利用了顶点的身份。这是通过引入1≤i≤c, 1≤v≤n的边缘概率来实现的,其中í指定发送顶点的组成员,ν指定接收顶点的身份。此外,考虑群体隶属度是随机的,参数为(í>í)í=io。我们提出了基于EM算法的参数估计方法,并讨论了潜在群体隶属度的恢复。引入了一个伴模型,即特定于接收方的块模型,其中1≤u≤n, 1 < j < c的边概率(≠uj)取决于接收有向边的顶点的隶属度。我们将这两种模型应用于几组社交网络数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Social Structure
Journal of Social Structure Social Sciences-Sociology and Political Science
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
24 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信