The purpose of this study is to explore how the multimode network approach can be used to analyse network patterns derived from student mobility flows. We define a tripartite network based on a three-mode data structure, consisting of Italian provinces of residence, universities and fields of study, with student exchanges representing the links between them. A comparison of algorithms for detecting communities from tripartite networks based on modularity optimization is provided, revealing relevant information about the phenomenon under analysis over time. The findings are applied to a real dataset containing micro-level longitudinal information on Italian university students’ careers.
Community detection in tripartite networks of university student mobility flows
Vitale Maria Prosperina
Membro del Collaboration Group
;Giuseppe GiordanoMembro del Collaboration Group
;
2021-01-01
Abstract
The purpose of this study is to explore how the multimode network approach can be used to analyse network patterns derived from student mobility flows. We define a tripartite network based on a three-mode data structure, consisting of Italian provinces of residence, universities and fields of study, with student exchanges representing the links between them. A comparison of algorithms for detecting communities from tripartite networks based on modularity optimization is provided, revealing relevant information about the phenomenon under analysis over time. The findings are applied to a real dataset containing micro-level longitudinal information on Italian university students’ careers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.