Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases
Article Category: Research Paper
Publié en ligne: 01 sept. 2017
Pages: 59 - 78
Reçu: 17 mai 2016
Accepté: 26 juin 2016
DOI: https://doi.org/10.20309/jdis.201620
Mots clés
© 2016 Raf Guns
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
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.