Title
Predictive characteristics of co-authorship networks : comparing the unweighted, weighted and bipartite cases
Author
Faculty/Department
Faculty of Social Sciences. Instructional and Educational Sciences
Faculty of Social Sciences. Communication Sciences
Publication type
article
Publication
Subject
Documentation and information
Source (journal)
Journal of Data and Information Science
Volume/pages
1(2016) :3 , p. 59-78
ISSN
2096-157X
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/3d8f4a/135107.pdf
Handle