RT Conference Proceedings
T1 A new community detection problem based on bipolar fuzzy measures
A1 Gutiérrez García-Pardo, Inmaculada
A1 Gómez González, Daniel
A1 Castro Cantalejo, Javier
A1 Espínola Vílchez, María Rosario
AB In social network research, one of the most important analysis is community detection. Fuzzy uncertainty appears clearly when modeling real situations by means of networks. Nevertheless, most of the algorithms used to detect communities in graphs represent them as something crisp. Due to its speed and efficiency, Louvain algorithm is one of the most popular methods used to find clusters in crisp networks. In this study, we propose a modification of it, based on the incorporation of a bipolar fuzzy measure defined over the nodes of the network. Our proposal is based on the use of the Shapley value, which is considered to measure the importance of each node.
SN 978-3-030-88817-6
SN 1860-949X
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/107837
UL https://hdl.handle.net/20.500.14352/107837
LA eng
NO Gutiérrez, Inmaculada, Daniel Gómez, Javier Castro, y Rosa Espínola. «A New Community Detection Problem Based on Bipolar Fuzzy Measures». En Studies in Computational Intelligence, 955:91-99. Springer Science and Business Media Deutschland GmbH, 2022. https://doi.org/10.1007/978-3-030-88817-6_11
NO Colección de libros: Studies in Computational Intelligence ((SCI,volume 955))
NO Gobierno de España. Secretaría de Estado de Investigacion, Desarrollo e Innovacion
DS Docta Complutense
RD 9 abr 2025