改进KGAT的恐怖组织空间行为预测方法

doi:10.3778/j.issn.1673-9418.2406012

摘要/Abstract

摘要： 当前，恐怖主义已成为影响世界和平与发展的重要因素。分析恐怖袭击事件以提取有用信息，并预测恐怖组织的空间行为成为当下研究的热点之一。虽然已经有较多对于恐怖袭击空间预测的研究，但大多数方法在构建恐怖组织空间行为关系网络并显示提取高阶关系方面仍有提升空间。为解决这一问题，将恐怖组织空间行为预测问题建模为由恐怖组织-袭击地点交互网络和袭击地点知识图构成的恐怖组织空间行为协同知识图，提出了一种基于改进KGAT的恐怖组织空间行为预测方法KGCE。该方法根据恐怖组织与袭击地点之间的交互关系及袭击地点的知识图谱，分别构建了恐怖组织-袭击地点交互网络及袭击地点知识图，通过将这两种网络结合，提出了一种基于社会-空间关系的恐怖组织空间行为协同知识图；改进了KGAT结构，在嵌入层中引入TransE模型以缓解模型过拟合问题，同时整体模型以端到端的方式，有效地对恐怖组织空间行为协同知识图中的高阶关系进行了建模。在公开数据集上通过与五个主流竞争基线进行对比，实验结果和分析表明，KGCE在恐怖组织空间行为预测准确度方面高于现有基线，其召回率最高提升7.14%，验证了该框架的有效性与正确性。

关键词: 恐怖组织, 注意力机制, 空间行为预测, 推荐系统

Abstract: At present, terrorism has become an important factor affecting world peace and development. Analyzing terrorist attacks to extract useful information and predict the spatial behavior of terrorist organizations has become one of the hotspots of current research. Although there have been many studies on the spatial prediction of terrorist attacks, most methods still have room for improvement in constructing the spatial behavior relationship network of terrorist organizations and extracting higher-order relationships. In order to solve this problem, the spatial behavior prediction problem of terrorist organization is modeled as a collaborative knowledge graph composed of terrorist organization-attack site interaction network and attack site knowledge graph. This paper proposes a spatial behavior prediction method KGCE (knowledge graph recommendation of TransE) of terrorist organization based on improved KGAT (knowledge graph attention network for recommendation). Firstly, according to the interactive relationship between the terrorist organization and the attack site and the knowledge graph of the attack site, the terrorist organization-attack site interaction network and the attack site knowledge graph are constructed respectively. By combining the two networks, a terrorist organization spatial behavior collaborative knowledge graph based on socio-spatial relationship is proposed. Secondly, the structure of KGAT is improved, and the TransE model is introduced into the embedding layer to alleviate the problem of model overfitting. At the same time, the overall model effectively models the high-order relationship in the collaborative knowledge graph of spatial behavior of terrorist organizations in an end-to-end manner. Compared with five mainstream competitive baselines on the public dataset, the experimental results and analysis show that KGCE is higher than the existing baselines in the prediction accuracy of terrorist organization spatial behavior, and its recall is improved by up to 7.14%, which verifies the effectiveness and correctness of the proposed framework.

Key words: terrorist organizations, attention mechanism, spatial behavior prediction, recommendation system

韩竹轩, 卜凡亮, 侯智文, 齐彬廷, 曹恩奇. 改进KGAT的恐怖组织空间行为预测方法[J]. 计算机科学与探索, 2025, 19(7): 1918-1930.

HAN Zhuxuan, BU Fanliang, HOU Zhiwen, QI Binting, CAO Enqi. Improved KGAT Method for Predicting Spatial Behavior of Terrorist Organizations[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(7): 1918-1930.

参考文献

[1] MINU K, LIMEESH M, JOHN C J. Wavelet neural networks for nonlinear time series analysis[J]. Applied Mathematical Sciences, 2010, 4: 2485-2495.
[2] LIU Q, WU S, WANG L, et al. Predicting the next location: a recurrent model with spatial and temporal contexts[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2016, 30(1): 194-200.
[3] MENG X, NIE L Y, SONG J P. Big data-based prediction of terrorist attacks[J]. Computers & Electrical Engineering, 2019, 77: 120-127.
[4] UDDIN M I, ZADA N, AZIZ F, et al. Prediction of future terrorist activities using deep neural networks[J]. Complexity, 2020(1): 1373087.
[5] VERMA C, MALHOTRA S, VERMA V. Predictive modeling of terrorist attacks using machine learning[J]. International Journal of Pure and Applied Mathematics, 2018, 119(15): 6-12.
[6] HUANG S L. Regional terrorism-related safety risk evaluation and its determinants research based on DRF-KPCA and nonlinear panel model[J]. Advances in Applied Mathematics, 2021, 10(4): 974-988.
[7] HAO M M, JIANG D, DING F Y, et al. Simulating spatio-temporal patterns of terrorism incidents on the Indochina Peninsula with GIS and the random forest method[J]. ISPRS International Journal of Geo-Information, 2019, 8(3): 133.
[8] WANG X, HE X N, CAO Y X, et al. KGAT: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 950-958.
[9] CLARK N J, DIXON P M. Modeling and estimation for self-exciting spatio-temporal models of terrorist activity[J]. The Annals of Applied Statistics, 2018, 12(1): 633-653.
[10] ALVES L G A, RIBEIRO H V, RODRIGUES F A. Crime prediction through urban metrics and statistical learning[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 505: 435-443.
[11] CHAINEY S, TOMPSON L, UHLIG S. The utility of hotspot mapping for predicting spatial patterns of crime[J]. Security Journal, 2008, 21(1): 4-28.
[12] 罗澜峻, 祁超, 王红卫, 等. 基于LSTM模型的恐怖袭击事件发生时间预测[J]. 系统工程学报, 2020, 35(2): 163-172.
LUO L J, QI C, WANG H W, et al. Prediction of terrorist attack events time based on LSTM model[J]. Journal of Systems Engineering, 2020, 35(2): 163-172.
[13] LIN Z K, DOU Y M, LI J P. Analysis model of terrorist attacks based on big data[C]//Proceedings of the 2020 Chinese Control and Decision Conference. Piscataway: IEEE, 2020: 3622-3628.
[14] 赵晔辉, 柳林, 王海龙, 等. 知识图谱推荐系统研究综述[J]. 计算机科学与探索, 2023, 17(4): 771-791.
ZHAO Y H, LIU L, WANG H L, et al. Survey of know-ledge graph recommendation system research[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(4): 771-791.
[15] YU X, REN X, GU Q Q, et al. Collaborative filtering with entity similarity regularization in heterogeneous information networks[C]//Proceedings of the IJCAI-13 HINA Workshop, 2013: 2612-2618.
[16] ZHANG S, WANG W H, FORD J, et al. Learning from incomplete ratings using non-negative matrix factorization[C]//Proceedings of the 2006 SIAM International Conference on Data Mining, 2006: 549-553.
[17] YU X, REN X, SUN Y Z, et al. Recommendation in heterogeneous information networks with implicit user feedback[C]//Proceedings of the 7th ACM Conference on Recommender Systems. New York: ACM, 2013: 347-350.
[18] YU X, REN X, SUN Y Z, et al. Personalized entity recommendation: a heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York: ACM, 2014: 283-292.
[19] SHI C, ZHANG Z Q, LUO P, et al. Semantic path based personalized recommendation on weighted heterogeneous information networks[C]//Proceedings of the 24th ACM International Conference on Information and Knowledge Management. New York: ACM, 2015: 453-462.
[20] XIAN Y K, FU Z H, MUTHUKRISHNAN S, et al. Reinforcement knowledge graph reasoning for explainable recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2019: 285-294.
[21] 田萱, 陈杭雪. 推荐任务中知识图谱嵌入应用研究综述[J]. 计算机科学与探索, 2022, 16(8): 1681-1705.
TIAN X, CHEN H X. Survey on applications of knowledge graph embedding in recommendation tasks[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1681-1705.
[22] ANTOINE B, NICOLAS U, ALBERTO G, et al. Transla-ting embeddings for modeling multi-relational data[C]//Advances?in?Neural?Information?Processing?Systems?26,?2013: 2787-2795.
[23] WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2014, 28(1): 1112-1119.
[24] LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 29(1): 2181-2187.
[25] JI G L, HE S Z, XU L H, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2015: 687-696.
[26] YANG B S, YIH W, HE X D, et al. Embedding entities and relations for learning and inference in knowledge bases[EB/OL]. [2024-04-10]. https://arxiv.org/abs/1412.6575.
[27] ZHANG F Z, YUAN N J, LIAN D F, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 353-362.
[28] WANG H W, ZHANG F Z, HOU M, et al. SHINE: signed heterogeneous information network embedding for sentiment link prediction[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining. New York: ACM, 2018: 592-600.
[29] YANG D Q, GUO Z K, WANG Z Y, et al. A knowledge-enhanced deep recommendation framework incorporating GAN-based models[C]//Proceedings of the 2018 IEEE International Conference on Data Mining. Piscataway: IEEE, 2018: 1368-1373.
[30] CAO Y X, WANG X, HE X N, et al. Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences[C]//Proceedings of the 2019 World Wide Web Conference. New York: ACM, 2019: 151-161.
[31] WANG H W, ZHANG F Z, WANG J L, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York: ACM, 2018: 417-426.
[32] TANG X L, WANG T Y, YANG H Z, et al. AKUPM: attention-enhanced knowledge-aware user preference model for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 1891-1899.
[33] QU Y R, BAI T, ZHANG W N, et al. An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation[C]//Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. New York: ACM, 2019: 1-9.
[34] 王旭, 庞巍, 王喆. 异构信息网络中基于元结构的协同过滤算法[J]. 计算机科学, 2019, 46(S1): 397-401.
WANG X, PANG W, WANG Z. MetaStruct-CF: a meta structure based collaborative filtering algorithm in heterogeneous information networks[J]. Computer Science, 2019, 46(S1): 397-401.
[35] 杨玉基, 许斌, 胡家威, 等. 一种准确而高效的领域知识图谱构建方法[J]. 软件学报, 2018, 29(10): 2931-2947.
YANG Y J, XU B, HU J W, et al. Accurate and efficient method for constructing domain knowledge graph[J]. Journal of Software, 2018, 29(10): 2931-2947.
[36] 许鑫冉, 王腾宇, 鲁才. 图神经网络在知识图谱构建与应用中的研究进展[J]. 计算机科学与探索, 2023, 17(10): 2278-2299.
XU X R, WANG T Y, LU C. Research progress of graph neural network in knowledge graph construction and application[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2278-2299.
[37] THOMAS N K, WELLING M. Semi-supervised classification with graph convolutional networks[C]//Proceedings of the 5th International Conference on Learning Representations, 2017.
[38] WILLIAM L H, REX Y, JURE L. Inductive representation learning on large graphs[C]//Advances in Neural Information Processing Systems 30, 2017: 1024-1034.
[39] AL M, AY H, AY N. Rectifier nonlinearities improve neural network acoustic models[C]//Proceedings of the 30th International Conference on Machine Learning, 2013: 3-8.
[40] STEFFEN R, CHRISTOPH F, ZENO G, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 2012 Conference on Uncertainty in Artificial Intelligence, 2012: 452-461.
[41] HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering[C]//Proceedings of the 2017 Web Conference, 2017: 173-182.
[42] YANG J H, CHEN C M, WANG C J, et al. HOP-Rec: high-order proximity for implicit recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems. New York: ACM, 2018: 140-144.
[43] AI Q Y, AZIZI V, CHEN X, et al. Learning heterogeneous knowledge base embeddings for explainable recommendation[J]. Algorithms, 2018, 11(9): 137.
[44] STEFFEN R, ZENO G, CHRISTOPH F, et al. Fast context-aware recommendations with factorization machines[C]// Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2011: 635-644.
[45] YANG Y H, HUANG C, XIA L H, et al. Knowledge graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 1434-1443.