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 Giordano
Membro 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.
2021
9788855183406
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4769104
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