Quantifying social network dynamics

Radosław Michalski, Piotr Bródka, Przemyslaw Kazienko, K. Juszczyszyn
{"title":"Quantifying social network dynamics","authors":"Radosław Michalski, Piotr Bródka, Przemyslaw Kazienko, K. Juszczyszyn","doi":"10.1109/CASoN.2012.6412380","DOIUrl":null,"url":null,"abstract":"The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.","PeriodicalId":431370,"journal":{"name":"2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CASoN.2012.6412380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.
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
量化社会网络动态
大多数社会网络的动态特性要求对网络的演化进行建模,以便对其动态进行复杂的分析。下面的文章重点讨论了用图差分元组(Graph Differential Tuple)来定义网络快照之间的差异。这些差异使我们能够计算不同的距离度量以及研究变化的速度。本文通过对真实社会网络数据的实验研究,提出了四种独立的度量方法。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Boosting Optimum-Path Forest clustering through harmony Search and its applications for intrusion detection in computer networks Graph-based cross-validated committees ensembles Automatic sentiment analysis of Twitter messages Identifying focal patterns in social networks Ontology-based Negotiation of security requirements in cloud
×
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