Compensatory fuzzy logic for intelligent social network analysis

• Maikel Y Leyva-Vázquez ^[1] ; Rafael Bello-Lara ^[1] ; Rafael Alejandro Espín-Andrade ^[2]
  1. [1] Universidad de las Ciencias Informáticas

     Universidad de las Ciencias Informáticas

     Cuba

     
  2. [2] Universidad de Occidente

     Universidad de Occidente

     México

     
• Localización: Revista Cubana de Ciencias Informáticas, ISSN-e 2227-1899, Vol. 8, Nº. 4, 2014
• Idioma: español
• Títulos paralelos:
  • Lógica difusa compensatoria para el análisis inteligente de redes sociales
• Enlaces
  • Texto completo
• Resumen
  • español

    La teoría de los grafos difusos ha ganado en visibilidad para el análisis de redes sociales. En este trabajo se discute el rol de las relaciones difusas y su papel en el modelado de redes sociales. En el artículo se presenta una propuesta para extender el marco de trabajo de la lógica difusa al análisis inteligente de las redes sociales usando las propiedades de robustez e interpretabilidad asociadas a la lógica difusa compensatoria. Mediante este enfoque es analizada la importancia de los caminos teniendo en cuenta la longitud y la fortaleza de la conexión entre nodos de la red. Los resultados obtenidos resultan más consistentes con la forma de tomar decisiones en los humanos. Adicionalmente se presenta un estudio de caso basado en el análisis de una red de coautoría mostrando la aplicabilidad de la propuesta. El principal resultado obtenido radica en un nuevo modelo para el análisis de redes sociales basado en la lógica difusa compensatoria brindando resultados más robustos y permitiendo la compensación. Adicionalmente el modelo contribuye al uso del lenguaje en el proceso de análisis de redes sociales.

  • English

    Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with our model are more consistent with the way human make decisions. Additionally a case study to illustrate the applicability of the proposal on a coauthorship network is developed. Our main outcome is a new model for social network analysis based on compensatory fuzzy logic that gives more robust results and allows compensation. Moreover this approach makes emphasis in using language for social network analysis.

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