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

Raf Guns

doi:10.20309/jdis.201620

Journal of Data and Information Science >

2016 , Vol. 1 >Issue 3: 59 - 78

DOI: https://doi.org/10.20309/jdis.201620

Research Paper

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

Raf Guns

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Centre for R&D Monitoring (ECOOM), University of Antwerp, Antwerp 2020, Belgium

Received date: 2016-05-17

Revised date: 2016-06-15

Online published: 2016-06-26

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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.

http://ir.las.ac.cn/handle/12502/8732

Key words： Network evolution; Link prediction; Weighted networks; Bipartite networks; Two-mode networks

Cite this article

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 . DOI: 10.20309/jdis.201620

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