Fuzzy Measures: A solution to deal with community detection problems for networks with additional information

Issue title: Special section: Decision Making Using Intelligent and Fuzzy Techniques

Guest editors: Cengiz Kahraman

Article type: Research Article

Authors: Gutiérrez, Inmaculada^a | Gómez, Daniel^{a; b} | Castro, Javier^{a; b} | Espínola, Rosa^{a; b}

Affiliations: [a] Faculty of Statistics, Complutense University, Madrid | [b] Institute of Health Assessment, Complutense University, Madrid

Correspondence: [*] Corresponding author. Inmaculada Gutiérrez, Faculty of Statistics, Complutense University, Madrid. E-mail: [email protected].

Abstract: In this work we introduce the notion of the weighted graph associated with a fuzzy measure. Having a finite set of elements between which there exists an affinity fuzzy relation, we propose the definition of a group based on that affinity fuzzy relation between the individuals. Then, we propose an algorithm based on the Louvain’s method to deal with community detection problems with additional information independent of the graph. We also provide a particular method to solve community detection problems over extended fuzzy graphs. Finally, we test the performance of our proposal by means of some detailed computational tests calculated in several benchmark models.

Keywords: Fuzzy clustering, networks, community detection, fuzzy graph

DOI: 10.3233/JIFS-189091

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6217-6230, 2020