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Title: Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases
Author: Raf Guns
Source: Journal of Data and Information Science
Issued Date: 2016-09-18
Volume: 1, Issue:3, Pages:59-78
Keyword: Network evolution ; Link prediction ; Weighted networks ; Bipartite networks ; Two-mode networks
Subject: 新闻学与传播学 ; 图书馆、情报与文献学
Indexed Type: 其他
DOI: 10.20309/jdis.201620
Corresponding Author: Raf Guns (E-mail: raf.guns@uantwerpen.be).
DOC Type: Research Papers
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.
English 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.
Related URLs: 查看原文
Language: 英语
Citation statistics:
Content Type: 期刊论文
URI: http://ir.las.ac.cn/handle/12502/8732
Appears in Collections:Journal of Data and Information Science_Journal of Data and Information Science-2016 _期刊论文

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description.institution: Centre for R&D Monitoring (ECOOM), University of Antwerp, Antwerp 2020, Belgium

Recommended Citation:
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.
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