网络中的鲁棒性(robin):一个用于社区比较和验证的R包

R J. Pub Date : 2021-02-05 DOI:10.32614/rj-2021-040
V. Policastro, D. Righelli, A. Carissimo, L. Cutillo, I. Feis
{"title":"网络中的鲁棒性(robin):一个用于社区比较和验证的R包","authors":"V. Policastro, D. Righelli, A. Carissimo, L. Cutillo, I. Feis","doi":"10.32614/rj-2021-040","DOIUrl":null,"url":null,"abstract":"In network analysis, many community detection algorithms have been developed, however, their implementation leaves unaddressed the question of the statistical validation of the results. Here we present robin(ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"17 1","pages":"292"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"ROBustness In Network (robin): an R Package for Comparison and Validation of Communities\",\"authors\":\"V. Policastro, D. Righelli, A. Carissimo, L. Cutillo, I. Feis\",\"doi\":\"10.32614/rj-2021-040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In network analysis, many community detection algorithms have been developed, however, their implementation leaves unaddressed the question of the statistical validation of the results. Here we present robin(ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset.\",\"PeriodicalId\":20974,\"journal\":{\"name\":\"R J.\",\"volume\":\"17 1\",\"pages\":\"292\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R J.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32614/rj-2021-040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R J.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2021-040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

在网络分析中,已经开发了许多社区检测算法,然而,它们的实现没有解决结果的统计验证问题。在这里,我们提出robin(鲁棒性网络),这是一个R包,用于评估通过一种或多种方法发现的网络社区结构的鲁棒性,以给出其可靠性的指示。该过程首先检测由一组算法发现的社区结构是否具有统计显著性,然后在同一图上比较选定的两种检测算法,以选择更适合感兴趣网络的算法。我们在美国大学橄榄球基准数据集上演示了我们的包的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ROBustness In Network (robin): an R Package for Comparison and Validation of Communities
In network analysis, many community detection algorithms have been developed, however, their implementation leaves unaddressed the question of the statistical validation of the results. Here we present robin(ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset.
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