A Graph Neural Network-Based Role Classification in Criminal Networks

Vedat Dogan; Steven Prestwich; Barry O’Sullivan

doi:10.1007/978-3-031-94956-2_11

A Graph Neural Network-Based Role Classification in Criminal Networks

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

Understanding the roles of individuals in terrorist networks is an important task in counter-terrorism. This paper presents the first application of graph neural networks to this task. We apply our approach to a real-world terrorist network representing three different ideologies and nine specific groups. We demonstrate the challenges associated with this task and present the framework using graph neural networks and their advantages in this context.

Original language	English
Title of host publication	International Conference on Computational Science and Computational Intelligence
Pages	142-157
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-031-94956-2_11
Publication status	Published - 2025
Event	11th International Conference on Computational Science and Computational Intelligence, CSCI 2024 - Las Vegas, United States Duration: 11 Dec 2024 → 13 Dec 2024

Publication series

Name	Communications in Computer and Information Science ((CCIS,volume 2510))

Conference

Conference	11th International Conference on Computational Science and Computational Intelligence, CSCI 2024
Country/Territory	United States
City	Las Vegas
Period	11/12/24 → 13/12/24

Keywords

Counter-terrorism
Graph Neural Networks
Node Classification
Role Identification
Terrorist Networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-94956-2_11

Cite this

@inproceedings{efa3da54030641f89ca247039af81176,

title = "A Graph Neural Network-Based Role Classification in Criminal Networks",

abstract = "Understanding the roles of individuals in terrorist networks is an important task in counter-terrorism. This paper presents the first application of graph neural networks to this task. We apply our approach to a real-world terrorist network representing three different ideologies and nine specific groups. We demonstrate the challenges associated with this task and present the framework using graph neural networks and their advantages in this context.",

keywords = "Counter-terrorism, Graph Neural Networks, Node Classification, Role Identification, Terrorist Networks",

author = "Vedat Dogan and Steven Prestwich and Barry O{\textquoteright}Sullivan",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024 ; Conference date: 11-12-2024 Through 13-12-2024",

year = "2025",

doi = "10.1007/978-3-031-94956-2\_11",

language = "English",

series = "Communications in Computer and Information Science ((CCIS,volume 2510))",

pages = "142--157",

booktitle = "International Conference on Computational Science and Computational Intelligence",

}

Dogan, V , Prestwich, S & O’Sullivan, B 2025, A Graph Neural Network-Based Role Classification in Criminal Networks. in International Conference on Computational Science and Computational Intelligence. Communications in Computer and Information Science ((CCIS,volume 2510)), pp. 142-157, 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024, Las Vegas, United States, 11/12/24. https://doi.org/10.1007/978-3-031-94956-2_11

A Graph Neural Network-Based Role Classification in Criminal Networks. / Dogan, Vedat ; Prestwich, Steven ; O’Sullivan, Barry.
International Conference on Computational Science and Computational Intelligence. 2025. p. 142-157 (Communications in Computer and Information Science ((CCIS,volume 2510))).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

TY - GEN

T1 - A Graph Neural Network-Based Role Classification in Criminal Networks

AU - Dogan, Vedat

AU - Prestwich, Steven

AU - O’Sullivan, Barry

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Understanding the roles of individuals in terrorist networks is an important task in counter-terrorism. This paper presents the first application of graph neural networks to this task. We apply our approach to a real-world terrorist network representing three different ideologies and nine specific groups. We demonstrate the challenges associated with this task and present the framework using graph neural networks and their advantages in this context.

AB - Understanding the roles of individuals in terrorist networks is an important task in counter-terrorism. This paper presents the first application of graph neural networks to this task. We apply our approach to a real-world terrorist network representing three different ideologies and nine specific groups. We demonstrate the challenges associated with this task and present the framework using graph neural networks and their advantages in this context.

KW - Counter-terrorism

KW - Graph Neural Networks

KW - Node Classification

KW - Role Identification

KW - Terrorist Networks

UR - https://www.scopus.com/pages/publications/105014512178

U2 - 10.1007/978-3-031-94956-2_11

DO - 10.1007/978-3-031-94956-2_11

M3 - Conference proceeding

AN - SCOPUS:105014512178

T3 - Communications in Computer and Information Science ((CCIS,volume 2510))

SP - 142

EP - 157

BT - International Conference on Computational Science and Computational Intelligence

T2 - 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024

Y2 - 11 December 2024 through 13 December 2024

ER -