Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases
Raf Guns; Raf Guns (E-mail: raf.guns@uantwerpen.be).
2016-09-18
发表期刊Journal of Data and Information Science
卷号1期号:3页码:59-78
摘要
Purpose: This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.
Design/methodology/approach: We compare three types of networks: unweighted networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested.
Findings: Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases.
Research limitations: Only two relatively small case studies are considered.
Practical implications: The study suggests that future link prediction studies on co-occurrence networks should consider using the bipartite network as a training network.
Originality/value: This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.
;
Purpose: This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.
Design/methodology/approach: We compare three types of networks: unweighted networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested.
Findings: Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases.
Research limitations: Only two relatively small case studies are considered.
Practical implications: The study suggests that future link prediction studies on co-occurrence networks should consider using the bipartite network as a training network.
Originality/value: This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.
文章类型Research Papers
关键词Network Evolution Link Prediction Weighted Networks Bipartite Networks Two-mode Networks
学科领域新闻学与传播学 ; 图书馆、情报与文献学
DOI10.20309/jdis.201620
URL查看原文
收录类别其他
语种英语
引用统计
文献类型期刊论文
条目标识符http://ir.las.ac.cn/handle/12502/8732
专题Journal of Data and Information Science_Journal of Data and Information Science-2016
通讯作者Raf Guns (E-mail: raf.guns@uantwerpen.be).
作者单位Centre for R&D Monitoring (ECOOM), University of Antwerp, Antwerp 2020, Belgium
推荐引用方式
GB/T 7714
Raf Guns,Raf Guns . Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases[J]. Journal of Data and Information Science,2016,1(3):59-78.
APA Raf Guns,&Raf Guns .(2016).Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases.Journal of Data and Information Science,1(3),59-78.
MLA Raf Guns,et al."Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases".Journal of Data and Information Science 1.3(2016):59-78.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
20160305.pdf(1258KB)期刊论文作者接受稿开放获取CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Raf Guns]的文章
[Raf Guns (E-mail: raf.guns@uantwerpen.be).]的文章
百度学术
百度学术中相似的文章
[Raf Guns]的文章
[Raf Guns (E-mail: raf.guns@uantwerpen.be).]的文章
必应学术
必应学术中相似的文章
[Raf Guns]的文章
[Raf Guns (E-mail: raf.guns@uantwerpen.be).]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。