RT Conference Proceedings
T1 A new community detection algorithm based on fuzzy measures
A1 Gutiérrez García-Pardo, Inmaculada
A1 Gómez González, Daniel
A1 Castro Cantalejo, Javier
A1 Espínola Vílchez, María Rosario
AB Community detection problems are one of the most important topics in social network analysis. Most of the algorithms and techniques that find communities in a network, model and represent is as something crisp. However, there exist many real situations in which fuzzy uncertainty appears in a natural way when the network is modeled. In this work, we present a modification of the well-known Louvain algorithm for crisp network that allows us to deal with fuzzy information in the network. In particular, we incorporate to the classical Louvain algorithm the use of fuzzy measures for the nodes of the graph. We also incorporate to the classical method the use of Shapley value to measure the importance of each node. We define the affinity among a pair of nodes as how each node of the pair is affected by the absence of the other one.
PB Springer
SN 2194-5357
YR 2020
FD 2020
LK https://hdl.handle.net/20.500.14352/129911
UL https://hdl.handle.net/20.500.14352/129911
LA eng
NO Gutiérrez, I., Gómez, D., Castro, J., Espínola, R. (2020). A New Community Detection Algorithm Based on Fuzzy Measures. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_18
DS Docta Complutense
RD 22 mar 2026