A methodology for restructuring networks by using Markov Random Fields


Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.


open access article


universal decision making model, redesigning networks, Markov random fields


García Cabello, J., Castillo, P.A., Aguilar-Luzón, M. Chiclana, F., Herrera Viedma, E. (2021) A methodology for restructuring networks by using Markov Random Fields. Mathematics, 9(12), 1389.


Research Institute

Institute of Artificial Intelligence (IAI)