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Assessing the missing data problem in criminal network analysis using forensic DNA data

Datos Bibliográficos

ID18925322
AutoresSabine De Moor (0000-0001-8395-2207, Ghent University, autor de correspondencia), Christophe Vandeviver (0000-0001-9714-7006, Ghent University), Tom Vander Beken (0000-0002-1596-5070, Ghent University)
Año2020
Volumen61
Páginas99-106
Fecha de publicación2020-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Networks (JOURNAL)
Identificadores de la revistaISSN: 0378-8733 • E-ISSN: 1879-2111
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.socnet.2019.09.003
OpenAlexW2981889664
IdiomaEN
Citas recibidas4
Referencias citadas32

Missing data is pertinent to criminal networks due to the hidden nature of crime. Generally, researchers evaluate the impact of incomplete network data by extracting or adding nodes and/or edges from a known network. Statistics on this reduced or completed network are then compared with statistics from the known network. In this study, we integrate police data on known offenders with DNA data on unknown offenders. Statistics from the integrated dataset (‘known network’) are compared with statistics from the police data (‘reduced network’). Networks with both known and unknown offenders are bigger but also have a different structure to networks with only known offenders

Data mining · Data science · Machine learning · Missing data · Statistics · Computer Science · Crime Patterns and Interventions · Crime, Illicit Activities, and Governance · HIV, Drug Use, Sexual Risk · Mathematics

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Obras citantes distintas4
Citas por año0,67
Intervalo de citas2020 - 2026 (7)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 4
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