Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases

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	Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases
	Raf Guns
刊名	journal of data and information science
	2016-09-18
卷号	1 期号:3 页码:59-78
关键词	Network evolution Link prediction Weighted networks Bipartite networks Two-mode networks
通讯作者	raf guns (e-mail: raf.guns@uantwerpen.be).
中文摘要	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.
英文摘要	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.
学科主题	新闻学与传播学 ; 图书馆、情报与文献学
收录类别	其他
原文出处	http://www.chinalibraries.net
语种	英语
内容类型	期刊论文
源URL	[http://ir.las.ac.cn/handle/12502/8732]
专题	文献情报中心_Journal of Data and Information Science_Journal of Data and Information Science-2016
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. 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.(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."Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases".journal of data and information science 1.3(2016):59-78.