利用RDS树的抽样补全估计社会网络中的顶点测度。

Bilal Khan, Kirk Dombrowski, Ric Curtis, Travis Wendel
{"title":"利用RDS树的抽样补全估计社会网络中的顶点测度。","authors":"Bilal Khan,&nbsp;Kirk Dombrowski,&nbsp;Ric Curtis,&nbsp;Travis Wendel","doi":"10.4236/sn.2015.41001","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of \"estimating connectivity from spanning tree completions\" (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC's approach, which is to estimate network properties themselves without deciding on the final edge set.</p>","PeriodicalId":57107,"journal":{"name":"社交网络(英文)","volume":"4 1","pages":"1-16"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380167/pdf/","citationCount":"1","resultStr":"{\"title\":\"Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees.\",\"authors\":\"Bilal Khan,&nbsp;Kirk Dombrowski,&nbsp;Ric Curtis,&nbsp;Travis Wendel\",\"doi\":\"10.4236/sn.2015.41001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of \\\"estimating connectivity from spanning tree completions\\\" (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC's approach, which is to estimate network properties themselves without deciding on the final edge set.</p>\",\"PeriodicalId\":57107,\"journal\":{\"name\":\"社交网络(英文)\",\"volume\":\"4 1\",\"pages\":\"1-16\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380167/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"社交网络(英文)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4236/sn.2015.41001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"社交网络(英文)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/sn.2015.41001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文提出了一种从不完全数据集获取网络属性的新方法。与缺失数据相关的问题是社交网络分析中众所周知的绊脚石。“从生成树补全中估计连通性”(ECSTC)的方法是专门设计用于解决只有已知网络生成树的情况,例如通过应答驱动抽样(RDS)获得的生成树。利用从度信息派生的重复随机补全,该方法放弃了通常试图获得最终边或顶点名单的步骤,而是旨在从生成树本身概率地估计顶点的网络中心属性。在本文中,我们讨论了缺失数据的问题,并描述了我们的补全方法的协议,最后给出了一个实验的结果,在这个实验中,我们使用了从一个预先知道特征的图中采样的生成树来估计图相关的顶点属性。结果表明,与研究人员之前的假设相比,欣喜若狂的方法在从有限的数据集中获得个体的网络中心特性方面更有希望。这种方法代表了与过去处理缺失数据的策略的突破,这些策略主要是寻求完成图的方法,而不是狂喜的方法,这是在不决定最终边缘集的情况下估计网络属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees.

This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of "estimating connectivity from spanning tree completions" (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC's approach, which is to estimate network properties themselves without deciding on the final edge set.

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
自引率
0.00%
发文量
105
期刊最新文献
Methods for measuring diffusion of a social media-based health intervention. Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees. Assessment of Random Recruitment Assumption in Respondent-Driven Sampling in Egocentric Network Data.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1