Hongzhu Fu , Yutao Wei , Gege Chen , Xing He , Qiang Gao , Fan Zhou
{"title":"基于类型感知周期性异质性的可解释犯罪预测增广图信息瓶颈","authors":"Hongzhu Fu , Yutao Wei , Gege Chen , Xing He , Qiang Gao , Fan Zhou","doi":"10.1016/j.ipm.2025.104227","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate crime prediction is crucial for proactive law enforcement, enhancing public safety, and optimizing resource allocation. However, existing solutions struggle with two key challenges: (1) crime periodicity heterogeneity, where different types of crimes exhibit distinct periodic patterns; and (2) lacking of post-hoc explainability, as the black-box effect of deep neural models makes it challenging to interpret the model performance. To respond to these concerns, we present EX-Crime, a novel solution for crime prediction with an inherently explainable view. By following the merits of information bottleneck (IB) principles, we introduce an augmented graph information bottleneck learning block that is coupled with an integrated crime pattern modeling to capture spatial–temporal patterns of crimes. Moreover, a new crime type-aware periodicity learning, employing Fast Fourier Transform and autocorrelation techniques, is proposed to identify the periodic patterns of distinct crime for enhanced temporal modeling. Our extensive experiments on real-world datasets demonstrate that EX-Crime gains substantial improvements in prediction accuracy and interpretability, uncovering its potential to significantly advance crime understanding across diverse urban settings.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104227"},"PeriodicalIF":6.9000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Augmented graph information bottleneck with type-aware periodicity heterogeneity for explainable crime prediction\",\"authors\":\"Hongzhu Fu , Yutao Wei , Gege Chen , Xing He , Qiang Gao , Fan Zhou\",\"doi\":\"10.1016/j.ipm.2025.104227\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate crime prediction is crucial for proactive law enforcement, enhancing public safety, and optimizing resource allocation. However, existing solutions struggle with two key challenges: (1) crime periodicity heterogeneity, where different types of crimes exhibit distinct periodic patterns; and (2) lacking of post-hoc explainability, as the black-box effect of deep neural models makes it challenging to interpret the model performance. To respond to these concerns, we present EX-Crime, a novel solution for crime prediction with an inherently explainable view. By following the merits of information bottleneck (IB) principles, we introduce an augmented graph information bottleneck learning block that is coupled with an integrated crime pattern modeling to capture spatial–temporal patterns of crimes. Moreover, a new crime type-aware periodicity learning, employing Fast Fourier Transform and autocorrelation techniques, is proposed to identify the periodic patterns of distinct crime for enhanced temporal modeling. Our extensive experiments on real-world datasets demonstrate that EX-Crime gains substantial improvements in prediction accuracy and interpretability, uncovering its potential to significantly advance crime understanding across diverse urban settings.</div></div>\",\"PeriodicalId\":50365,\"journal\":{\"name\":\"Information Processing & Management\",\"volume\":\"62 6\",\"pages\":\"Article 104227\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing & Management\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306457325001682\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457325001682","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Augmented graph information bottleneck with type-aware periodicity heterogeneity for explainable crime prediction
Accurate crime prediction is crucial for proactive law enforcement, enhancing public safety, and optimizing resource allocation. However, existing solutions struggle with two key challenges: (1) crime periodicity heterogeneity, where different types of crimes exhibit distinct periodic patterns; and (2) lacking of post-hoc explainability, as the black-box effect of deep neural models makes it challenging to interpret the model performance. To respond to these concerns, we present EX-Crime, a novel solution for crime prediction with an inherently explainable view. By following the merits of information bottleneck (IB) principles, we introduce an augmented graph information bottleneck learning block that is coupled with an integrated crime pattern modeling to capture spatial–temporal patterns of crimes. Moreover, a new crime type-aware periodicity learning, employing Fast Fourier Transform and autocorrelation techniques, is proposed to identify the periodic patterns of distinct crime for enhanced temporal modeling. Our extensive experiments on real-world datasets demonstrate that EX-Crime gains substantial improvements in prediction accuracy and interpretability, uncovering its potential to significantly advance crime understanding across diverse urban settings.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.