  Data collected in criminal investigations may suffer from: (i)
incompleteness, due to the covert nature of criminal organisations; (ii)
incorrectness, caused by either unintentional data collection errors and
intentional deception by criminals; (iii) inconsistency, when the same
information is collected into law enforcement databases multiple times, or in
different formats. In this paper we analyse nine real criminal networks of
different nature (i.e., Mafia networks, criminal street gangs and terrorist
organizations) in order to quantify the impact of incomplete data and to
determine which network type is most affected by it. The networks are firstly
pruned following two specific methods: (i) random edges removal, simulating the
scenario in which the Law Enforcement Agencies (LEAs) fail to intercept some
calls, or to spot sporadic meetings among suspects; (ii) nodes removal, that
catches the hypothesis in which some suspects cannot be intercepted or
investigated. Finally we compute spectral (i.e., Adjacency, Laplacian and
Normalised Laplacian Spectral Distances) and matrix (i.e., Root Euclidean
Distance) distances between the complete and pruned networks, which we compare
using statistical analysis. Our investigation identified two main features:
first, the overall understanding of the criminal networks remains high even
with incomplete data on criminal interactions (i.e., 10% removed edges);
second, removing even a small fraction of suspects not investigated (i.e., 2%
removed nodes) may lead to significant misinterpretation of the overall
network.
