2026-06-27T10:23:07Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/244722024-11-25T15:31:02Zcom_20.500.14352_14col_20.500.14352_15

Gómez González, Daniel Rodríguez González, Juan Tinguaro Yáñez Gestoso, Francisco Javier Montero De Juan, Francisco Javier 2023-06-18T06:52:37Z 2023-06-18T06:52:37Z 2016 Gómez, D., Tinguaro Rodríguez, J., Yáñez, J., Montero, J.: A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions. International Journal of Approximate Reasoning. 74, 88-107 (2016). https://doi.org/10.1016/j.ijar.2016.03.003 0888-613X 10.1016/j.ijar.2016.03.003 https://hdl.handle.net/20.500.14352/24472 https//doi.org/10.1016/j.ijar.2016.03.003 http://www.sciencedirect.com/science/article/pii/S0888613X16300305 One of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measuring the quality of a fuzzy community detection output based on n-dimensional grouping and overlap functions. Moreover, the proposed modularity measure generalizes the classical Girvan–Newman (GN) modularity for crisp community detection problems and also for crisp overlapping community detection problems. Therefore, it can be used to compare partitions of different nature (i.e. those composed of classical, overlapping and fuzzy communities). Particularly, as is usually done with the GN modularity, the proposed measure may be used to identify the optimal number of communities to be obtained by any network clustering algorithm in a given network. We illustrate this usage by adapting in this way a well-known algorithm for fuzzy community detection problems, extending it to also deal with overlapping community detection problems and produce a ranking of the overlapping nodes. Some computational experiments show the feasibility of the proposed approach to modularity measures through n-dimensional overlap and grouping functions. eng restricted access A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions journal article