重叠社区的发现和改进

Rong Yang
{"title":"重叠社区的发现和改进","authors":"Rong Yang","doi":"10.29007/hdl3","DOIUrl":null,"url":null,"abstract":"Decomposing a network into communities is one of the most used techniques in network science. Modularity is typically used to measure the goodness of such a decomposition. In this paper we develop a method which allows us to begin with a crisp decomposition (no overlaps) and move to an overlapping decomposition while increasing the modularity. We also show that the same technique can be used to improve existing overlapping decompositions.","PeriodicalId":93549,"journal":{"name":"EPiC series in computing","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Title Overlapping Communities Discovery and Improvement\",\"authors\":\"Rong Yang\",\"doi\":\"10.29007/hdl3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Decomposing a network into communities is one of the most used techniques in network science. Modularity is typically used to measure the goodness of such a decomposition. In this paper we develop a method which allows us to begin with a crisp decomposition (no overlaps) and move to an overlapping decomposition while increasing the modularity. We also show that the same technique can be used to improve existing overlapping decompositions.\",\"PeriodicalId\":93549,\"journal\":{\"name\":\"EPiC series in computing\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EPiC series in computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29007/hdl3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EPiC series in computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29007/hdl3","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

将网络分解为社区是网络科学中最常用的技术之一。模块化通常用于度量这种分解的好坏。在本文中,我们开发了一种方法,允许我们从一个清晰的分解(没有重叠)开始,并在增加模块化的同时移动到一个重叠的分解。我们还表明,同样的技术可以用于改进现有的重叠分解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Title Overlapping Communities Discovery and Improvement
Decomposing a network into communities is one of the most used techniques in network science. Modularity is typically used to measure the goodness of such a decomposition. In this paper we develop a method which allows us to begin with a crisp decomposition (no overlaps) and move to an overlapping decomposition while increasing the modularity. We also show that the same technique can be used to improve existing overlapping decompositions.
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
CiteScore
1.60
自引率
0.00%
发文量
0
期刊最新文献
ARCH-COMP23 Category Report: Hybrid Systems Theorem Proving ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics ARCH-COMP23 Repeatability Evaluation Report ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
×
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