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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-10072019-101053


Tipo di tesi
Tesi di laurea magistrale
Autore
GENNARI, SARA
URN
etd-10072019-101053
Titolo
Missing links prediction in bipartite networks: mathematical analysis and application to e-commerce data
Dipartimento
MATEMATICA
Corso di studi
MATEMATICA
Relatori
relatore Prof. Romito, Marco
Parole chiave
  • link prediction
  • similarity based methods
Data inizio appello
25/10/2019
Consultabilità
Completa
Riassunto
Missing link prediction is a widely studied task of network analysis. It concerns the prediction of new links in a partially observed graph.
The thesis explores the problem from two sides: the mathematical analysis of some classical methods for link prediction and the application of these methods in a real world e-commerce network.
In the first part we present several similarity based indices grouped by the type of topological information they require and focusing on their mathematical formulation.
In the second part we analyze how missing link prediction algorithms previously presented perform in a network generated from a e-commerce data. In particular, such a network has a natural bipartite structure, that lead us to study link prediction on its projections.
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