Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or sub...
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| Contributors: | ; ; ; |
| Format: | Electronic Article |
| Language: | English |
| Published: |
2016
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| In: |
PLOS ONE
Year: 2016 |
| Online Access: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| Check availability: | HBZ Gateway |
| Keywords: |
MARC
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| 520 | |a The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activitie | ||
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