基于时空空间多图形卷积网络的省级一天级恐怖主义风险预测
Lanjun Luo1, Boxiao Li2, Chao Qi2
1School of Management, North Sichuan Medical College, Nanchong, China.
概括
由于恐怖主义的传染性,预测恐怖主义风险具有挑战性. 本研究引入了一个扩展的时空图卷积网络 (STGCN),通过分析省际恐怖主义动态来预测日常风险.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 人工智能的人工智能
背景情况:
- 预测恐怖主义风险对于有效的反恐战略至关重要.
- 恐怖主义风险表现出复杂的时空传染性特征,受到省际攻击和内部/外部因素的影响.
- 现有的模型很难捕捉到恐怖主义传播中固有的多维,非欧几里德关系.
研究的目的:
- 提出一种基于时空图卷积网络 (STGCN) 的新型扩展方法,用于预测日常恐怖主义风险.
- 为了建模恐怖主义风险在各省的复杂传染性扩散.
- 通过结合多维时空相关性来提高恐怖主义风险预测的准确性.
主要方法:
- 开发了一个扩展的时空图卷积网络 (STGCN),包含了长期短期记忆和自我注意层,用于时间动态.
- 构建了三个图形结构 (距离,根源相似性,自我激发) 来表示省际传染过程.
- 利用一维卷积神经网络内核和光谱图卷积模块,分别捕捉时间和空间特征.
主要成果:
- 与其他机器学习模型相比,提议的扩展STGCN方法在预测恐怖主义风险方面表现出更高的有效性.
- 对阿富汗恐怖袭击数据 (2005-2020年) 的实验结果验证了该模型的预测能力.
- 该研究强调了为准确预测风险而捕捉各省之间全面的时空相关性的至关重要.
结论:
- 扩展的STGCN为理解和预测恐怖主义风险扩散提供了一个强大的工具.
- 调查结果为反恐管理提供了宝贵的见解,强调长期根源原因缓解和短期局势预防.
- 准确的恐怖主义风险预测需要采用整体方法,考虑相互关联的时空因素.
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