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SUMMARY:RESEARCH SEMINAR Link prediction in knowledge networks using exogenous and endogenous attributes: a machine learning approach
DESCRIPTION:Presenter: Antonio Zinilli and Giovanni Cerulli\, CNR-IRCRES \n  \nDiscussant: Mike Thelwall\, University of Wolverhampton \n  \nAbstract \nWe propose a supervised machine learning approach to predict partnership formation between universities. We focus 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. \nWe 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\, we 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\, we find 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. \n\nREGISTRATION FORM\n					\n					                \n				\n			\n				\n				\n				\n                \n                \n                \n				\n					\n					\n                                            \n                    					 \n    Email Address* \n     \n \n \n    TITLE* \n     \n \n \n    LASTNAME* \n     \n \n \n    FIRSTNAME* \n     \n \n \n    COUNTRY* \n     \n \n \n    ORGANIZATION* \n     \n \n \n    POSITION* \n     \n \nYour e-mail address is only used to send you information about the activities of RISIS2. You can always use the unsubscribe link included in the communication. \n\n\nI accept the terms and conditions\n \n\n \n     \n \n				\n			\n			\n			\n  \n  \n 
URL:https://www.risis2.eu/event/research-seminar-link-prediction-in-knowledge-networks-using-exogenous-and-endogenous-attributes-a-machine-learning-approach/
CATEGORIES:Research Seminars
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