Identifying a Criminal's Network of Trust

TL;DR

This paper presents a novel algorithm that combines shortest paths and centrality measures to identify criminal connections within large trust networks derived from email data, aiding criminal investigations.

Contribution

The paper introduces a new shortest paths network search algorithm that effectively isolates criminal connections in large trust networks using centrality measures.

Findings

Successfully isolated criminal connections from Enron email data.

Demonstrated the algorithm's effectiveness in large-scale trust networks.

Enhanced network analysis for criminal investigation purposes.

Abstract

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.