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

IF 6.9 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongzhu Fu , Yutao Wei , Gege Chen , Xing He , Qiang Gao , Fan Zhou
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引用次数: 0

摘要

准确的犯罪预测对积极执法、加强公共安全、优化资源配置至关重要。然而,现有的解决方案面临两个关键挑战:(1)犯罪周期异质性,不同类型的犯罪表现出不同的周期模式;(2)缺乏事后可解释性,因为深度神经模型的黑箱效应使得对模型性能的解释具有挑战性。为了回应这些问题,我们提出了EX-Crime,一个具有内在可解释观点的犯罪预测的新解决方案。通过遵循信息瓶颈(IB)原则的优点,我们引入了一个增强图信息瓶颈学习块,该块与集成的犯罪模式建模相结合,以捕获犯罪的时空模式。此外,提出了一种新的犯罪类型感知周期学习方法,采用快速傅立叶变换和自相关技术来识别不同犯罪的周期模式,以增强时间建模。我们在真实世界数据集上的大量实验表明,前犯罪在预测准确性和可解释性方面取得了实质性的进步,揭示了其在不同城市环境中显著推进犯罪理解的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
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.
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