Periodically Diluted BEGNN Model of Corruption Perception

  • Mario González
  • David DominguezEmail author
  • Guillermo Jerez
  • Odette Pantoja
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)


This study evaluates the performance provided by a Blume-Emery-Griffiths neural network (BEGNN) for two datasets of corruption indicators, namely the Corruption Perceptions Index and the Global Corruption Barometer. Bi-lineal and bi-quadratic terms are added to the Hamiltonian of the model, as well as for the order parameters to measure the network retrieval efficiency. The network is tested for different noise levels of the patterns’ initial state during the retrieval phase in order to measure the robustness of the network and its basin of attraction. The network connectivity is diluted periodically and its performance is tested for different levels of dilution. The network is analyzed in terms of the pattern load, mixing the real corruption patterns with random patterns in order to assess the change from retrieval to non-retrieval phases.


Three-state neural network Corruption Perceptions Index Global Corruption Barometer 



This work has been supported by MINECO TIN2014-54580-R, TIN2017-84452-R, and by UAM-Santander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mario González
    • 1
  • David Dominguez
    • 2
    Email author
  • Guillermo Jerez
    • 2
  • Odette Pantoja
    • 3
  1. 1.SI2 LabUniversidad de las AméricasQuitoEcuador
  2. 2.Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain
  3. 3.FCAEscuela Politécnica NacionalQuitoEcuador

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