在社交计算和深度学习技术下,用于犯罪预测的社交网络分析.
1School of Humanities, Beijing University of Chinese Medicine, Beijing, 102488, China.
Scientific reports
|January 6, 2026
概括
基于账户关联 (KPI-AA) 的新的关键人身份识别模型有效地识别了犯罪网络中的关键人物. 该模型使用图形神经网络 (GNN) 来改进对犯罪行为和网络漏洞的预测.
科学领域:
- 社交计算社会计算
- 网络科学 网络科学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 犯罪网络具有复杂的结构和高度隐蔽.
- 识别关键数据和预测这些网络中的行为是具有挑战性的.
- 社会计算和深度学习为发现隐藏的网络动态提供了潜在的解决方案.
研究的目的:
- 提出一种用于识别犯罪网络中关键人物的新型模式.
- 提高犯罪行为和网络漏洞的预测能力.
- 利用图形神经网络进行深入的网络分析.
主要方法:
- 开发了基于账户关联 (KPI-AA) 的关键人身份识别模型.
- 结合了当地的邻居相似性和全球边缘之间的差距指标.
- 利用图形神经网络 (GNN) 进行节点表征和网络分析.
主要成果:
- 与模拟网络的基线模型相比,KPI-AA显示出更高的传播动态.
- 该模型在网络稳定性测试中显示出弹性,在显著节点移除后保持连接.
- 在Twitter数据集上获得了0.467的肯德尔的tau系数,表明强大的排名一致性.
结论:
- KPI-AA有效地识别核心成员并预测犯罪网络中的行为.
- 该模型在传播速度,漏洞识别和排名一致性方面具有优势.
- 证明了在社会保障和刑事调查中实际部署的可扩展性和可行性.
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