情报杂志2026，Vol.45Issue(4)：40-48,9.DOI:10.3969/j.issn.1002-1965.2026.04.006
基于时序知识图谱推理的网络极端化风险行为预警研究
Early Warning of Online Extremism Risk Behaviors Based on Temporal Knowledge Graph Reasoning
申舒亦 1王一帆 1卜凡亮1
作者信息
- 1. 中国人民公安大学信息网络安全学院 北京 100038
- 折叠
摘要
Abstract
[Purpose]To enhance the capability of identifying and providing early warning of online extremism risk behaviors in public se-curity intelligence analysis,this study aims to uncover the temporal patterns underlying individuals'evolving tendencies toward extremism expressed in cyberspace.[Method]This study develops an early-warning framework for online extremism risk behaviors based on tempo-ral knowledge graph reasoning and proposes HOO-TKE,a temporal knowledge graph extrapolation model featuring an offline-online memory-fusion reasoning mechanism.The model consists of an offline knowledge reasoning module,an online dynamic reasoning mod-ule,and a historical memory integration module.By leveraging cross-temporal feature fusion and multi-level semantic modeling,the pro-posed approach addresses the limitations of traditional models in capturing long-and short-term dependencies and ensuring semantic conti-nuity in behavioral evolution.Experiments are conducted on three internationally recognized temporal event datasets widely used for fore-casting social conflicts and risk events.[Result/Conclusion]Experimental results show that HOO-TKE achieves an average improvement of 1.96%in MRR,2.17%in Hits@1,2.07%in Hits@3,and 1.08%in Hits@10,demonstrating the effectiveness and interpretability of the model in temporal knowledge reasoning and risk behavior early warning.关键词
时序知识图谱/网络极端化风险行为/预警建模/HOO-TKE模型Key words
temporal knowledge graph/online extremism risk behavior/early warning modeling/HOO-TKE model分类
社会科学引用本文复制引用
申舒亦,王一帆,卜凡亮..基于时序知识图谱推理的网络极端化风险行为预警研究[J].情报杂志,2026,45(4):40-48,9.
