Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Graph Neural Network-Based Role Classification in Criminal Networks

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Vedat Dogan,
  • Steven Prestwich,
  • Barry O’Sullivan

摘要

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.