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Title
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Predictive characteristics of co-authorship networks : comparing the unweighted, weighted and bipartite cases
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Journal of Data and Information Science. - [S.l.]
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Publication
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[S.l.]
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Chinese Academy of Sciences
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2016
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ISSN
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2096-157X
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DOI
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10.20309/JDIS.201620
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Volume/pages
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1
:3
(2016)
, p. 59-78
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Full text (open access)
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