Complicity functions for detecting organized crime rings

Mateos Caballero, Alfonso; Jiménez Martín, Antonio y Vicente Cestero, Eloy (2016). Complicity functions for detecting organized crime rings. En: "13th International Conference on Modeling Decisions for Artificial Intelligence, (MDAI 2016)", 19-21 Sep 2016, Sant Julià de Lòria, Andorra. ISBN 978-3-319-45656-0. pp. 205-216. https://doi.org/10.1007/978-3-319-45656-0 17.

Descripción

Título: Complicity functions for detecting organized crime rings
Autor/es:
  • Mateos Caballero, Alfonso
  • Jiménez Martín, Antonio
  • Vicente Cestero, Eloy
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 13th International Conference on Modeling Decisions for Artificial Intelligence, (MDAI 2016)
Fechas del Evento: 19-21 Sep 2016
Lugar del Evento: Sant Julià de Lòria, Andorra
Título del Libro: Modelling decisions for Artificial Intelligence
Fecha: 2016
ISBN: 978-3-319-45656-0
Volumen: 9880
Materias:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (723kB) | Vista Previa

Resumen

Graph theory is an evident paradigm for analyzing social networks, which are the main tool for collective behavior research, addressing the interrelations between members of a more or less well-defined community. Particularly, social network analysis has important implications in the fight against organized crime, business associations with fraudulent purposes or terrorism. Classic centrality functions for graphs are able to identify the key players of a network or their intermediaries. However, these functions provide little information in large and heterogeneous graphs. Often the most central elements of the network (usually too many) are not related to a collective of actors of interest, such as be a group of drug traffickers or fraudsters. Instead, its high centrality is due to the good relations of these central elements with other honorable actors. In this paper we introduce complicity functions, which are capable of identifying the intermediaries in a group of actors, avoiding core elements that have nothing to do with this group. These functions can classify a group of criminals according to the strength of their relationships with other actors to facilitate the detection of organized crime rings. The proposed approach is illustrated by a real example provided by the Spanish Tax Agency, including a network of 835 companies, of which eight were fraudulent.

Más información

ID de Registro: 46641
Identificador DC: http://oa.upm.es/46641/
Identificador OAI: oai:oa.upm.es:46641
Identificador DOI: 10.1007/978-3-319-45656-0 17
URL Oficial: https://link.springer.com/content/pdf/10.1007%2F978-3-319-45656-0.pdf
Depositado por: Memoria Investigacion
Depositado el: 06 Nov 2017 08:47
Ultima Modificación: 06 Nov 2017 08:47
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM