A Synthetic Network Generator for Covert Network Analytics

TL;DR

This paper introduces a synthetic network generator that creates anonymized, statistically similar covert networks for research and analysis, addressing data accessibility and privacy issues in studying illegal organizations.

Contribution

The authors develop a stochastic block model-based generator that preserves organizational structure, enabling robust analysis of covert networks without compromising privacy.

Findings

Generated networks are statistically similar to real covert networks.

Analysts can identify stable community structures under perturbations.

The approach allows for quantifying interdiction outcomes statistically.

Abstract

We study social networks and focus on covert (also known as hidden) networks, such as terrorist or criminal networks. Their structures, memberships and activities are illegal. Thus, data about covert networks is often incomplete and partially incorrect, making interpreting structures and activities of such networks challenging. For legal reasons, real data about active covert networks is inaccessible to researchers. To address these challenges, we introduce here a network generator for synthetic networks that are statistically similar to a real network but void of personal information about its members. The generator uses statistical data about a real or imagined covert organization network. It generates randomized instances of the Stochastic Block model of the network groups but preserves this network organizational structure. The direct use of such anonymized networks is for training on them the research and analytical tools for finding structure and dynamics of covert networks. Since these synthetic networks differ in their sets of edges and communities, they can be used as a new source for network analytics. First, they provide alternative interpretations of the data about the original network. The distribution of probabilities for these alternative interpretations enables new network analytics. The analysts can find community structures which are frequent, therefore stable under perturbations. They may also analyze how the stability changes with the strength of perturbation. For covert networks, the analysts can quantify statistically expected outcomes of interdiction. This kind of analytics applies to all complex network in which the data are incomplete or partially incorrect.