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Graph and Network Theory for the Analysis of Criminal Networks

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Data Science and Internet of Things

Part of the book series: Internet of Things ((ITTCC))

Abstract

Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridical acts, we have extracted data on the interactions among suspects within two Sicilian Mafia clans, obtaining two weighted undirected graphs. Then, we have investigated the roles of these weights on the criminal networks properties, focusing on two key features: weight distribution and shortest path length. We also present an experiment that aims to construct an artificial network which mirrors criminal behaviours. To this end, we have conducted a comparative degree distribution analysis between the real criminal networks, using some of the most popular artificial network models: Watts-Strogats, Erdős-Rényi, and Barabási-Albert, with some topology variations. This chapter will be a valuable tool for researchers who wish to employ social network analysis within their own area of interest.

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Notes

  1. 1.

    Source code are available at https://github.com/lcucav/criminal-nets.

  2. 2.

    https://networkx.github.io/documentation/networkx-1.9/reference/generators.html.

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Author information

Authors and Affiliations

  1. University of Derby, Derby, UK

    Lucia Cavallaro & Ovidiu Bagdasar

  2. University of Messina, Messina, Italy

    Pasquale De Meo & Giacomo Fiumara

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Antonio Liotta

Authors
  1. Lucia Cavallaro
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  2. Ovidiu Bagdasar
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  3. Pasquale De Meo
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  4. Giacomo Fiumara
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  5. Antonio Liotta
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Corresponding author

Correspondence to Lucia Cavallaro .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende (CS), Italy

    Giancarlo Fortino

  2. Edinburgh Napier University, Edinburgh, UK

    Antonio Liotta

  3. University of Calabria, Rende (CS), Italy

    Raffaele Gravina

  4. University of Catania, Catania, Italy

    Alessandro Longheu

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Cavallaro, L., Bagdasar, O., De Meo, P., Fiumara, G., Liotta, A. (2021). Graph and Network Theory for the Analysis of Criminal Networks. In: Fortino, G., Liotta, A., Gravina, R., Longheu, A. (eds) Data Science and Internet of Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-67197-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-67197-6_8

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