Social networks identification and analysis using call detail records

Khan, Muhammad Usman; Khan, Shoab Ahmed

doi:10.1145/1655925.1655959

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Each set of analysis integrating these variables carry their own rationale. Out-degree centrality measures explain the propagation of messages in a telephone communication network [41,42]. This is important for the present case study since our data are drawn from intercepted communications between criminal network participants.…”

Section: Centrality Measures and Criminal Behaviormentioning

confidence: 99%

Predicting sentencing outcomes with centrality measures

et al. 2013

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Despite their importance for stakeholders in the criminal justice system, few methods have been developed for determining which criminal behavior variables will produce accurate sentence predictions. Some approaches found in the literature resort to techniques based on indirect variables, but not on the social network behavior with exception of the work of Baker and Faulkner [ASR 58: 837–860, 1993]. Using information on the Caviar Network narcotics trafficking group as a real-world case, we attempt to explain sentencing outcomes employing the social network indicators. Specifically, we report the ability of centrality measures to predict a) the verdict (innocent or guilty) and b) the sentence length in years. We show that while the set of indicators described by Baker and Faulkner yields good predictions, introduction of the additional centrality measures generates better predictions. Some ideas for orienting future research on further improvements to sentencing outcome prediction are discussed.A pesar de la importancia para diferentes actores involucrados en el sistema judicial, se han desarrollados pocos métodos para determinar las variables del comportamiento organizado que permiten predecir las sentencias judiciales de redes criminales. Algunas aproximaciones encontradas en la literatura especializada usa variables indirectas al comportamiento organizado y no en el comportamiento en red de estas organizaciones. Nosotros usamos información real sobre un caso de red criminal real que operó en Montreal (Canadá) y analizamos la comunicación entre los miembros de la red para determinar si su comportamiento comunicacional permite predecir el veredicto así como los años de sentencia. Encontramos que los modelos de regresión obtenidos y las variables de centralidad nodal utilizadas por nosotros logra un mejor capacidad predictiva. Finalmente, se discuten algunas ideas dirigidas a mejorar la predicción de sentencias judiciales desde las medidas de redes sociales

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Section: Centrality Measures and Criminal Behaviormentioning

confidence: 99%