Multiple bipolar fuzzy measures: an application to community detection problems for networks with additional information

Gutiérrez García-Pardo, Inmaculada; Gómez González, Daniel; Castro Cantalejo, Javier; Espínola Vílchez, María Rosario

Multiple bipolar fuzzy measures: an application to community detection problems for networks with additional information

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Official URL

https://link.springer.com/article/10.2991/ijcis.d.201012.001

Publication date

2020

Authors

Gutiérrez García-Pardo, Inmaculada

Gómez González, Daniel

Castro Cantalejo, Javier

Espínola Vílchez, María Rosario

Publisher

Atlantis Press, Springer

Citations

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URI

https://hdl.handle.net/20.500.14352/100039

Citation

Gutiérrez, I., Gómez, D., Castro, J. et al. Multiple Bipolar Fuzzy Measures: An Application to Community Detection Problems for Networks with Additional Information. Int J Comput Intell Syst 13, 1636–1649 (2020). https://doi.org/10.2991/ijcis.d.201012.001

Abstract

In this paper we introduce the concept of multiple bipolar fuzzy measures as a generalization of a bipolar fuzzy measure. We also propose a new definition of a group, which is based on the multidimensional bipolar fuzzy relations of its elements. Taking into account this information, we provide a novel procedure (based on the well-known Louvain algorithm) to deal with community detection problems. This new method considers the multidimensional bipolar information provided by multiple bipolar fuzzy measures, as well as the information provided by a graph. We also give some detailed computational tests, obtained from the application of this algorithm in several benchmark models.