网络邻域分析

Michael D. Porter, Ryan Smith
{"title":"网络邻域分析","authors":"Michael D. Porter, Ryan Smith","doi":"10.1109/ISI.2010.5484781","DOIUrl":null,"url":null,"abstract":"We present a technique to represent the structure of large social networks through ego-centered network neighborhoods. This provides a local view of the network, focusing on the vertices and their kth order neighborhoods allowing discovery of interesting patterns and features of the network that would be hidden in a global network analysis. We present several examples from a corporate phone call network revealing the ability of our methods to discover interesting network behavior that is only available at the local level. In addition, we present an approach to use these concepts to identify abrupt or subtle anomalies in dynamic networks.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"70 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Network neighborhood analysis\",\"authors\":\"Michael D. Porter, Ryan Smith\",\"doi\":\"10.1109/ISI.2010.5484781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a technique to represent the structure of large social networks through ego-centered network neighborhoods. This provides a local view of the network, focusing on the vertices and their kth order neighborhoods allowing discovery of interesting patterns and features of the network that would be hidden in a global network analysis. We present several examples from a corporate phone call network revealing the ability of our methods to discover interesting network behavior that is only available at the local level. In addition, we present an approach to use these concepts to identify abrupt or subtle anomalies in dynamic networks.\",\"PeriodicalId\":434501,\"journal\":{\"name\":\"2010 IEEE International Conference on Intelligence and Security Informatics\",\"volume\":\"70 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Intelligence and Security Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISI.2010.5484781\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligence and Security Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISI.2010.5484781","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

我们提出了一种通过以自我为中心的网络邻域来表示大型社会网络结构的技术。这提供了网络的局部视图,重点关注顶点及其k阶邻域,从而发现可能隐藏在全局网络分析中的有趣模式和网络特征。我们给出了几个来自公司电话网络的例子,揭示了我们的方法发现仅在本地级别可用的有趣网络行为的能力。此外,我们提出了一种使用这些概念来识别动态网络中突然或微妙异常的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network neighborhood analysis
We present a technique to represent the structure of large social networks through ego-centered network neighborhoods. This provides a local view of the network, focusing on the vertices and their kth order neighborhoods allowing discovery of interesting patterns and features of the network that would be hidden in a global network analysis. We present several examples from a corporate phone call network revealing the ability of our methods to discover interesting network behavior that is only available at the local level. In addition, we present an approach to use these concepts to identify abrupt or subtle anomalies in dynamic networks.
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
0
×
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