The detection of criminal groups in real-world fused data: using the graph-mining algorithm “GraphExtract”

• Published in: Security informatics (Berlin) Vol. 7; no. 1; pp. 1 - 16
• Main Authors: Robinson, David, Scogings, Chris
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 17.08.2018; Springer Nature B.V
• Subjects: Computer Science; Crime; Data mining; Data Mining and Knowledge Discovery; Disruption; Information Systems and Communication Service; Intersections; Representations; Terrorism; Organised crime; Dark networks; Criminal analysis; Social network analysis; Risk detection; Crime; Criminal networks; SNA; Entity resolution; Graph mining
• ISSN: 2190-8532; 2190-8532
• DOI: 10.1186/s13388-018-0031-9

Law enforcement and intelligence agencies generally have access to a number of rich data sources, both structured and unstructured, and with the advent of high performing entity resolution it is now possible to fuse multiple heterogeneous datasets into an explicit generic data representation. But once this is achieved how should agencies go about attempting to exploit this data by proactively identifying criminal events and the actors responsible? The authors will outline an effective generic method that; computationally extracts minimally overlapping contextual subgraphs, then uses these subgraphs as the basis to construct a mesoscopic graph based on the intersections between the subgraphs, enabling knowledge discovery from these data representations for the purpose of maximally disrupting terrorism, organised crime and the broader criminal network.