RT Conference Proceedings
T1 A supervised approach to community detection problem: how to improve Louvain algorithm by considering fuzzy measures
A1 Barroso, María
A1 Gómez González, Daniel
A1 Gutiérrez García-Pardo, Inmaculada
AB Community detection problems are one of the most important problems in Social Network Analysis. Based on the Louvain algorithm, in this paper we propose a supervised technique to address the classic community detection problem in both directed and undirected networks. Our proposal is developed on the basis of extended fuzzy graphs, specifically paying attention to the notion of flow. We present a parametric and aggregation supervised approach that uses the flow capacity in terms of fuzzy information, in order to obtain realistic and global solutions, going one step further than local previous results. We evaluate the performance of that supervised technique by considering several benchmark and real-world networks. Taking into account the directed modularity, this new approach is developed under the machine learning paradigm, carrying through with two consecutive phases. The results obtained allow us to assert the goodness of our new supervised technique, beyond others existing algorithms
SN 9783031091728
SN 2367-3370
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/107835
UL https://hdl.handle.net/20.500.14352/107835
LA eng
NO Barroso, M., Gómez, D. y Gutiérrez, I. (2022) «A Supervised Approach to Community Detection Problem: How to Improve Louvain Algorithm by Considering Fuzzy Measures», en Lecture Notes in Networks and Systems. Springer Science and Business Media Deutschland GmbH, pp. 219-227. Disponible en: https://doi.org/10.1007/978-3-031-09173-5_28
NO Colección de libros: Lecture Notes in Networks and Systems (504 LNNS)
NO Ministerio de Ciencia, Innovación y Universidades (España)
DS Docta Complutense
RD 20 abr 2025