Combinations of affinity functions for different community detection algorithms in social networks
Fecha
2021Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.24251/HICSS.2022.265
Resumen
Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition ...
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Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are
designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to
combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms. [--]
Materias
Social network analysis,
Affinity functions,
Community detection,
Modularity,
Aggregation functions
Editor
University of Hawaii Press
Publicado en
Bui, T. X. (Ed.): Proceedings of the Hawaii International Conference on System Sciences, HICSS 2021. University of Hawaii Press, 2021, 2107 - 2114,
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
Versión del editor
Entidades Financiadoras
Javier Fumanal Idocin and Humberto
Bustince’s re-search has been supported
by the project PID2019-108392GBI00
(AEI/10.13039/501100011033).
Maria Minarová research has been funded by the project work was supported by the projects APVV-17-0066 andAPVV-18-0052.
Oscar Cordon’s research was supported by the Spanish Ministry of Science, Innovation and Universities under grant EXASOCO (PGC2018-101216-B-I00), including, European Regional Development Funds (ERDF).