Giovanni Cerulli
Antonio Zinilli
2022-03-14
<p>The researchers propose a supervised machine learning approach to predict partnership formation between universities. The focus is on successful joint R&D projects funded by Horizon 2020 programme in three research domains: Social Sciences and Humanities, Physical and Engineering Sciences, and Life Sciences.</p>
<p> </p>
<p>The researchers perform two connected analyses: link formation prediction, and feature importance detection. As for link prediction, using out-of-sample cross-validated accuracy and a set of network endogenous and exogenous attributes, the researchers obtain 90% prediction accuracy when both types of attributes are used, and around 65% when using only the exogenous ones. This proves that partnership predictive power is on average 25% larger for universities already incumbent in the programme than for newcomers. As for feature importance, by computing super-learner average partial effects and elasticities, the study finds that the endogenous attributes are the most relevant in affecting the probability to generate a link and observe a largely negative elasticity of the link probability to feature changes, fairly uniform across attributes and domains.</p>
https://doi.org/10.5281/zenodo.6351928
oai:zenodo.org:6351928
eng
Zenodo
https://zenodo.org/communities/risis
https://doi.org/10.5281/zenodo.6351927
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Machine Learning
R&D projects
Horizon 2020 programme
partnership formation
Universities
RISIS Research Seminar 16/ Link prediction in knowledge networks using exogenous and endogenous attributes: a machine learning approach
info:eu-repo/semantics/lecture