Tesi etd-11172025-090313 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
SARACINO, MARIO
URN
etd-11172025-090313
Titolo
Listening, Lyrics, and Links: A Multiplex Network Approach to Music Community Discovery
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Prof. Rossetti, Giulio
correlatore Citraro, Salvatore
correlatore Citraro, Salvatore
Parole chiave
- ai
- artificial intelligence
- community detection
- multilayer network
- multiplex network
- music discovery
- network science
- social network analysis
Data inizio appello
04/12/2025
Consultabilità
Completa
Riassunto
This thesis applies multiple network analysis to last.fm data (120,000 users and 4,028 artists) to detect music communities. We construct two-layer networks capturing behavioral similarity (shared listeners) and thematic similarity (lyrical content via textual forma mentis networks), comparing flattening, layer-by-layer (M-EMCD) and multilayer (Glouvain) community detection algorithms. The work reveals how communities emerge from the interaction between listening patterns and thematic preferences.
File
| Nome file | Dimensione |
|---|---|
| Saracino...V2_01.pdf | 13.40 Mb |
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