RT Journal Article
T1 From fuzzy information to community detection: an approach to social networks analysis with soft information
A1 Gutiérrez García-Pardo, Inmaculada
A1 Gómez González, Daniel
A1 Castro Cantalejo, Javier
A1 Espínola Vílchez, María Rosario
A2 Wierzchoń, Sławomir T.
AB On the basis of network analysis, and within the context of modeling imprecision or vague information with fuzzy sets, we propose an innovative way to analyze, aggregate and apply this uncertain knowledge into community detection of real-life problems. This work is set on the existence of one (or multiple) soft information sources, independent of the network considered, assuming this extra knowledge is modeled by a vector of fuzzy sets (or a family of vectors). This information may represent, for example, how much some people agree with a specific law, or their position against several politicians. We emphasize the importance of being able to manage the vagueness which usually appears in real life because of the common use of linguistic terms. Then, we propose a constructive method to build fuzzy measures from fuzzy sets. These measures are the basis of a new representation model which combines the information of a network with that of fuzzy sets, specifically when it comes to linguistic terms. We propose a specific application of that model in terms of finding communities in a network with additional soft information. To do so, we propose an efficient algorithm and measure its performance by means of a benchmarking process, obtaining high-quality results.
PB MDPI
YR 2022
FD 2022-11-19
LK https://hdl.handle.net/20.500.14352/100106
UL https://hdl.handle.net/20.500.14352/100106
LA eng
NO Gutiérrez, I.; Gómez, D.; Castro, J.; Espínola, R. From Fuzzy Information to Community Detection: An Approach to Social Networks Analysis with Soft Information. Mathematics 2022, 10, 4348. https://doi.org/10.3390/ math10224348
DS Docta Complutense
RD 21 ago 2024