Inductive and transductive link prediction for criminal network analysis

authored by

Zahra Ahmadi, Hoang H. Nguyen, Zijian Zhang, Dmytro Bozhkov, Daniel Kudenko, Maria Jofre, Francesco Calderoni, Noa Cohen, Yosef Solewicz

Abstract

The identification of potential offenders, who are more likely to form a new group and co-offend in a crime, plays an essential role in narrowing down law enforcement investigations and improving predictive policing. Once a crime is committed, focusing on linking it to previously reported crimes and reducing the inspections based on shreds of evidence and the behavior of offenders can also greatly help law enforcement agencies. However, classical investigative techniques are generally case-specific and rely mainly on police officers manually combining information from different sources. Therefore, automatic methods designed to support co-offender research and crime linkage would be beneficial. This paper proposes two graph-based machine learning frameworks to address these issues based on a burglary use case, the first being transductive link prediction, which seeks to predict emergent links between existing graph nodes (which represent offenders or criminal cases), and the other being inductive link prediction, where connections are found between a new case and existing nodes. Our experimental results show a prediction accuracy of 68.5% in co-offender prediction, a 75.83% predictive accuracy for transductive crime linkage, and up to 74.8% accuracy in inductive crime linkage.

Organisation(s)

L3S Research Centre

External Organisation(s)

Universita Cattolica del Sacro Cuore, Rome
Israel Police

Type

Article

Journal

Journal of computational science

Volume

72

ISSN

1877-7503

Publication date

09.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Theoretical Computer Science, Computer Science(all), Modelling and Simulation

Sustainable Development Goals

SDG 16 - Peace, Justice and Strong Institutions

Electronic version(s)

https://doi.org/10.1016/j.jocs.2023.102063 (Access: Closed)