Exploring the knowledge diffusion and research front of OWA operator: a main path analysis

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2023-05-16 DOI:10.1007/s10462-023-10462-y
Dejian Yu, Tianxing Pan, Zeshui Xu, Ronald R. Yager
{"title":"Exploring the knowledge diffusion and research front of OWA operator: a main path analysis","authors":"Dejian Yu,&nbsp;Tianxing Pan,&nbsp;Zeshui Xu,&nbsp;Ronald R. Yager","doi":"10.1007/s10462-023-10462-y","DOIUrl":null,"url":null,"abstract":"<div><p>In recent years, more and more attention is paid to the OWA operator in the academy. Growth curve analysis, which is often used in ecosystem studies, also indicates that this growth trend will continue. However, prior literature has not made a big picture to help researchers make clear of the development of this field by identifying the evolution path. The classic main path analysis is an excellent method combining quantitative analysis and qualitative analysis. We conducted the classic main path analysis and its variants on a citation network with 1474 papers to probe the development trajectories and research topics of OWA. We obtained several findings by constructing local and global main path, and multiple main paths. The path results indicate that weight generation and operator generalization run through the overall OWA domain, show that the multiple criteria decision making process assumed in the related research begins to be dynamic and multi-period, and reveal that some theories such as social network theory are introduced into the OWA operator and the applications are also greatly expanded.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 10","pages":"12233 - 12255"},"PeriodicalIF":10.7000,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-023-10462-y.pdf","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-023-10462-y","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 2

Abstract

In recent years, more and more attention is paid to the OWA operator in the academy. Growth curve analysis, which is often used in ecosystem studies, also indicates that this growth trend will continue. However, prior literature has not made a big picture to help researchers make clear of the development of this field by identifying the evolution path. The classic main path analysis is an excellent method combining quantitative analysis and qualitative analysis. We conducted the classic main path analysis and its variants on a citation network with 1474 papers to probe the development trajectories and research topics of OWA. We obtained several findings by constructing local and global main path, and multiple main paths. The path results indicate that weight generation and operator generalization run through the overall OWA domain, show that the multiple criteria decision making process assumed in the related research begins to be dynamic and multi-period, and reveal that some theories such as social network theory are introduced into the OWA operator and the applications are also greatly expanded.

Abstract Image

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
OWA算子知识扩散与研究前沿的探索——主路径分析
近年来,OWA操作员越来越受到学术界的重视。生态系统研究中经常使用的生长曲线分析也表明这种增长趋势将持续下去。然而,先前的文献并没有通过确定进化路径来帮助研究人员明确这一领域的发展。经典主路径分析法是一种定量分析与定性分析相结合的优秀方法。通过对1474篇文献的引文网络进行经典主路径及其变体分析,探讨OWA的发展轨迹和研究课题。通过构建局部主路、全局主路和多个主路,我们得到了一些结果。路径结果表明,权值生成和算子泛化贯穿于整个OWA域,表明相关研究中假设的多准则决策过程开始具有动态性和多周期性,揭示了社会网络理论等一些理论被引入OWA算子,应用范围也得到了极大拓展。
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
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
期刊最新文献
Federated learning design and functional models: survey A systematic literature review of recent advances on context-aware recommender systems Escape: an optimization method based on crowd evacuation behaviors A multi-strategy boosted bald eagle search algorithm for global optimization and constrained engineering problems: case study on MLP classification problems Innovative solution suggestions for financing electric vehicle charging infrastructure investments with a novel artificial intelligence-based fuzzy decision-making modelling
×
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