<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201620_s_006_w2aab2b8c62b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p> This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.</p></sec><sec id="j_jdis.201620_s_007_w2aab2b8c62b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis.201620_s_008_w2aab2b8c62b1b7b1aab1c15b3Aa"><h3>Findings</h3><p> 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.</p></sec><sec id="j_jdis.201620_s_009_w2aab2b8c62b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p> Only two relatively small case studies are considered.</p></sec><sec id="j_jdis.201620_s_010_w2aab2b8c62b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p> The study suggests that future link prediction studies on co-occurrence networks should consider using the bipartite network as a training network.</p></sec><sec id="j_jdis.201620_s_011_w2aab2b8c62b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p> This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.</p></sec></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/approachWe 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.

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

Journal of Data and Information Science

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...

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

Article Category: Research Paper

Pubblicato online: 01 set 2017

Pagine: 59 - 78

Ricevuto: 17 mag 2016

Accettato: 26 giu 2016

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

Parole chiave
Network evolution, Link prediction, Weighted networks, Bipartite networks, Two-mode networks

© 2016 Raf Guns

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Article Category: Research Paper

Pubblicato online: 01 set 2017

Pagine: 59 - 78

Ricevuto: 17 mag 2016

Accettato: 26 giu 2016

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

Parole chiaveNetwork evolution, Link prediction, Weighted networks, Bipartite networks, Two-mode networks

© 2016 Raf Guns

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Network evolution, Link prediction, Weighted networks, Bipartite networks, Two-mode networks