A new community detection problem based on bipolar fuzzy measures
No Thumbnail Available
Official URL
Full text at PDC
Publication date
2022
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Exportar
Citation
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
Abstract
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.
Description
Colección de libros: Studies in Computational Intelligence ((SCI,volume 955))