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
Source PublicationJournal of Data and Information Science
Volume1Issue:3Pages:59-78
Abstract
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.
SubtypeResearch Papers
KeywordNetwork Evolution Link Prediction Weighted Networks Bipartite Networks Two-mode Networks
Subject Area新闻学与传播学 ; 图书馆、情报与文献学
DOI10.20309/jdis.201620
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Indexed By其他
Language英语
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Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/8732
CollectionJournal of Data and Information Science_Journal of Data and Information Science-2016
Corresponding AuthorRaf Guns (E-mail: raf.guns@uantwerpen.be).
AffiliationCentre for R&D Monitoring (ECOOM), University of Antwerp, Antwerp 2020, Belgium
Recommended Citation
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.
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