Community structures and role detection in music networks

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We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the grow...

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Detalles Bibliográficos
Autores principales: Teitelbaum, T., Balenzuela, P., Cano, P., Buldú, J.M.
Formato: JOUR
Materias:
algorithm
automated pattern recognition
biological model
computer simulation
cooperation
interpersonal communication
music
procedures
social support
Algorithms
Communication
Computer Simulation
Cooperative Behavior
Models, Biological
Music
Pattern Recognition, Automated
Social Support
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_10541500_v18_n4_p_Teitelbaum
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_10541500_v18_n4_p_Teitelbaum
record_format dspace
spelling todo:paper_10541500_v18_n4_p_Teitelbaum2023-10-03T16:00:41Z Community structures and role detection in music networks Teitelbaum, T. Balenzuela, P. Cano, P. Buldú, J.M. algorithm automated pattern recognition biological model computer simulation cooperation interpersonal communication music procedures social support Algorithms Communication Computer Simulation Cooperative Behavior Models, Biological Music Pattern Recognition, Automated Social Support We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes. © 2008 American Institute of Physics. Fil:Teitelbaum, T. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Balenzuela, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_10541500_v18_n4_p_Teitelbaum
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic algorithm
automated pattern recognition
biological model
computer simulation
cooperation
interpersonal communication
music
procedures
social support
Algorithms
Communication
Computer Simulation
Cooperative Behavior
Models, Biological
Music
Pattern Recognition, Automated
Social Support
spellingShingle algorithm
automated pattern recognition
biological model
computer simulation
cooperation
interpersonal communication
music
procedures
social support
Algorithms
Communication
Computer Simulation
Cooperative Behavior
Models, Biological
Music
Pattern Recognition, Automated
Social Support
Teitelbaum, T.
Balenzuela, P.
Cano, P.
Buldú, J.M.
Community structures and role detection in music networks
topic_facet algorithm
automated pattern recognition
biological model
computer simulation
cooperation
interpersonal communication
music
procedures
social support
Algorithms
Communication
Computer Simulation
Cooperative Behavior
Models, Biological
Music
Pattern Recognition, Automated
Social Support
description We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes. © 2008 American Institute of Physics.
format JOUR
author Teitelbaum, T.
Balenzuela, P.
Cano, P.
Buldú, J.M.
author_facet Teitelbaum, T.
Balenzuela, P.
Cano, P.
Buldú, J.M.
author_sort Teitelbaum, T.
title Community structures and role detection in music networks
title_short Community structures and role detection in music networks
title_full Community structures and role detection in music networks
title_fullStr Community structures and role detection in music networks
title_full_unstemmed Community structures and role detection in music networks
title_sort community structures and role detection in music networks
url http://hdl.handle.net/20.500.12110/paper_10541500_v18_n4_p_Teitelbaum
work_keys_str_mv AT teitelbaumt communitystructuresandroledetectioninmusicnetworks
AT balenzuelap communitystructuresandroledetectioninmusicnetworks
AT canop communitystructuresandroledetectioninmusicnetworks
AT buldujm communitystructuresandroledetectioninmusicnetworks
_version_ 1807317991851819008

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