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Criminal networks analysis in missing data scenarios through graph distances.
Annamaria Ficara1,2, Lucia Cavallaro3, Francesco Curreri1,2
1DMI Department, University of Palermo, Palermo, Italy.
Plos One
|August 11, 2021
Summary
Criminal network analysis remains robust with incomplete interaction data. However, omitting even a small number of suspects can significantly distort understanding of criminal organizations.
Area of Science:
- Network Science
- Criminology
- Data Analysis
Background:
- Criminal investigations generate data prone to incompleteness, incorrectness, and inconsistency.
- Understanding the impact of data quality issues on criminal network analysis is crucial for law enforcement.
Purpose of the Study:
- To quantify the impact of incomplete data on criminal network analysis.
- To identify which types of criminal networks are most affected by data deficiencies.
- To assess the robustness of network analysis under simulated data loss scenarios.
Main Methods:
- Analysis of nine real-world criminal networks (Mafia, street gangs, terrorist organizations).
- Network pruning via random edge removal (simulating missed communications) and node removal (simulating uninvestigated suspects).
- Computation of spectral and matrix distances between complete and pruned networks.
Main Results:
- Criminal network understanding remains high even with up to 10% edge removal (incomplete interaction data).
- Removal of a small fraction of nodes (as low as 2%) can lead to significant misinterpretation of network structures.
- The impact of data incompleteness varies across different types of criminal networks.
Conclusions:
- Network analysis is relatively resilient to missing interaction data but highly sensitive to the omission of key individuals.
- Law enforcement strategies should prioritize comprehensive suspect investigation to avoid network misinterpretations.
- Robustness of criminal network analysis depends on both data completeness and the accurate identification of all relevant actors.
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