双模网络的一类关联指标*

Q2 Social Sciences
Frank Tutzauer
{"title":"双模网络的一类关联指标*","authors":"Frank Tutzauer","doi":"10.21307/JOSS-2019-024","DOIUrl":null,"url":null,"abstract":"Abstract An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors’ affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in the network. Although of high degree, in no reasonable interpretation would such an actor be considered highly affiliated with other actors in the network. At the other extreme, one might have an actor defined by a collection of events, all of which were attended by another actor(s), making the actor as enmeshed in the network as possible. Most actors will be between these extremes, with some events being shared by varying others, and some not. This article introduces a family of affiliation measures based on the entries of the co-occurrence matrix. After defining the measures, the cumulative distribution function of first-order affiliation is derived and expressed as a difference of binomials.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"14 1","pages":"1 - 19"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Family of Affiliation Indices for Two-Mode Networks*\",\"authors\":\"Frank Tutzauer\",\"doi\":\"10.21307/JOSS-2019-024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors’ affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in the network. Although of high degree, in no reasonable interpretation would such an actor be considered highly affiliated with other actors in the network. At the other extreme, one might have an actor defined by a collection of events, all of which were attended by another actor(s), making the actor as enmeshed in the network as possible. Most actors will be between these extremes, with some events being shared by varying others, and some not. This article introduces a family of affiliation measures based on the entries of the co-occurrence matrix. After defining the measures, the cumulative distribution function of first-order affiliation is derived and expressed as a difference of binomials.\",\"PeriodicalId\":35236,\"journal\":{\"name\":\"Journal of Social Structure\",\"volume\":\"14 1\",\"pages\":\"1 - 19\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Social Structure\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21307/JOSS-2019-024\",\"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-2019-024","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: 2

摘要

隶属网络由行动者和事件组成。行动者通过他们共同参加的事件而相互关联。本文介绍了一系列从属度量,这些度量捕获了网络中参与者的从属关系的程度。在一种极端情况下,可能有一个参与者参加了许多事件,但网络中的任何其他参与者都没有参加这些事件。虽然关联度很高,但在任何合理的解释中,这样的行为者都不会被认为与网络中的其他行为者有高度的关联。在另一种极端情况下,可能有一个由事件集合定义的参与者,所有这些事件都由另一个参与者参加,使参与者尽可能地卷入网络中。大多数参与者将处于这两个极端之间,一些事件由不同的其他人共同承担,而另一些则没有。本文介绍了一组基于共现矩阵条目的隶属度量。定义测度后,推导出一阶隶属关系的累积分布函数,并将其表示为二项之差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Family of Affiliation Indices for Two-Mode Networks*
Abstract An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors’ affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in the network. Although of high degree, in no reasonable interpretation would such an actor be considered highly affiliated with other actors in the network. At the other extreme, one might have an actor defined by a collection of events, all of which were attended by another actor(s), making the actor as enmeshed in the network as possible. Most actors will be between these extremes, with some events being shared by varying others, and some not. This article introduces a family of affiliation measures based on the entries of the co-occurrence matrix. After defining the measures, the cumulative distribution function of first-order affiliation is derived and expressed as a difference of binomials.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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