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Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
Giulia Berlusconi1, Francesco Calderoni1, Nicola Parolini2
1Università Cattolica del Sacro Cuore and Transcrime, Milano, Italy.
Plos One
|April 23, 2016
Summary
This study introduces a novel strategy for identifying missing links in criminal networks by analyzing network topology. The findings suggest that node similarity measures can effectively predict hidden connections, aiding investigations.
Area of Science:
- Network Science
- Social Network Analysis
- Criminal Network Analysis
Background:
- Link prediction is crucial for uncovering incomplete information in social and criminal networks.
- Criminal investigations inherently face data uncertainty due to evasion tactics and resource limitations.
Purpose of the Study:
- To develop a strategy for identifying missing links in criminal networks using topological analysis.
- To leverage 'marginal' links (removed during investigations) to infer hidden connections.
Main Methods:
- Topological analysis of criminal network data.
- Utilizing 'marginal' links (those removed during investigation) as indicators.
- Applying node similarity measures to predict missing links.
Main Results:
- Node similarity measures proved effective in characterizing missing links.
- Predicted links demonstrated a high likelihood of co-participation in illicit activities.
- Analysis of judicial documents validated the predicted connections.
Conclusions:
- The proposed strategy successfully identifies missing links in criminal networks.
- Node similarity is a key factor in predicting hidden criminal associations.
- This approach enhances investigative capabilities by revealing previously unknown connections.
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