基于图嵌入模型的犯罪组织成员关系预测  

Crime Organization Member Relationship Prediction Based on Graph Embedding Model

作　　者：袁立宁 邢中玉 杨国艺 罗恒雨 YUAN Li-ning;XING Zhong-yu;YANG Guo-yi;LUO Heng-yu(School of Information Technology,Guangxi Police College,Nanning 530028,Guangxi;School of Public Security Big Data Modern Industry,Guangxi Police College,Nanning 530028,Guangxi;School of Criminal Science and Technology,Guangxi Police College,Nanning 530028,Guangxi)

机构地区：[1]广西警察学院信息技术学院,广西南宁530028 [2]广西警察学院公安大数据现代产业学院,广西南宁530028 [3]广西警察学院刑事科学技术学院,广西南宁530028

出　　处：《电脑与电信》2024年第4期1-5,共5页Computer & Telecommunication

基　　金：广西哲学社会科学研究课题“城市盗窃犯罪态势感知技术及其应用研究”,项目编号:23FTQ005;广西高校中青年教师科研基础能力提升项目“基于深度学习的公安监控图像超分辨率重建技术应用研究”,项目编号:2024KY0902;广西壮族自治区公安厅专项课题“社交媒体网络安全问题及其治理研究”,项目编号:2023GAQN092;“基于网络溯源技术的网络犯罪取证研究”研究成果之一,项目编号:2023GAQN110。

摘　　要：随着社会的快速发展,犯罪行为愈发复杂多样化,群体性案件高发多发,使得基于犯罪学理论和案例研判的传统犯罪组织分析方法已无法满足情报工作的需求。因此,利用深度学习技术分析和挖掘犯罪组织特性,已成为数据警务工作的必然选择。本文使用图嵌入模型变分图自编码器(VGAE)对犯罪组织成员关系进行预测。模型的编码器部分提取犯罪组织结构特征并生成特征向量,解码器部分使用向量内积重构犯罪组织结构,进而预测犯罪组织中两个成员之间是否存在关联。为了评估VGAE在关系预测任务中的实验表现,在开源犯罪网络数据集Montagna上进行测试。实验结果表明,VGAE具备较高的预测性能,能够有效识别犯罪组织成员之间的潜在关系。With the rapid development of society,criminal activities have become increasingly complex and diversified,and mass cases become more frequent,which makes the traditional criminal organization analysis method based on criminology theory and case analysis no longer meet the needs of intelligence work.Therefore,utilizing deep learning technology to analyze and explore the characteristics of criminal organizations has become an essential approach for data policing efforts.This article utilizes the Variational Graph Autoencoder(VGAE)model to predict relationships among members of criminal organizations.The encoder part of the model extracts structural features of criminal organizations and generates feature vectors,while the decoder part reconstructs the criminal organization structure using vector inner products.This process helps predict whether there is a connection between two members of the criminal organization.To evaluate the experimental performance of VGAE in relationship prediction tasks,tests are conducted on the open-source crime network dataset Montagna.The experimental results show that VGAE demonstrates high predictive performance and can effectively identify potential relationships among members of criminal organizations.

关 键 词：图嵌入 犯罪组织 关系预测 图卷积网络 自编码器 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O157.5[自动化与计算机技术—控制科学与工程]