复杂网络的并行封闭中心性算法

K. Erciyes
{"title":"复杂网络的并行封闭中心性算法","authors":"K. Erciyes","doi":"10.1109/iisec54230.2021.9672334","DOIUrl":null,"url":null,"abstract":"Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A parallel closed centrality algorithm for complex networks\",\"authors\":\"K. Erciyes\",\"doi\":\"10.1109/iisec54230.2021.9672334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.\",\"PeriodicalId\":344273,\"journal\":{\"name\":\"2021 2nd International Informatics and Software Engineering Conference (IISEC)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Informatics and Software Engineering Conference (IISEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iisec54230.2021.9672334\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iisec54230.2021.9672334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

复杂网络规模很大,分析这些网络所需的方法与小型网络有很大不同。为了及时地对这些网络进行分析,需要并行处理。图中心性度量为评估这些网络的结构提供了方便的方法。我们回顾了主要的中心性算法,描述了封闭中心性在Python中的实现,提出了一个简单的并行封闭中心性算法,并给出了它在Python中的实现和得到的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A parallel closed centrality algorithm for complex networks
Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.
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