Detecting Key Offenders from Crime Incidents via Attributed Heterogeneous Network Learning-Reference-Cited by-同舟云学术

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Detecting Key Offenders from Crime Incidents via Attributed Heterogeneous Network Learning

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)
language:
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Author:

Li Zhe¹,Jing Xiao²,Cao Shanshan³,Zhang Tianfan¹,Wu Zhiang⁴

Affiliation:

1. Hubei Engineering University,School of Economics & Management,Xiaogan,China

2. Northwestern Polytechnical University,School of Cybersecurity,Xi’an,China

3. Nanjing University,School of Geography and Ocean Science,Nanjing,China

4. Nanjing Audit University,School of Information Engineering,Nanjing,China

Funder

National Natural Science Foundation of China

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10024507/10024490/10024529.pdf?arnumber=10024529

Reference24 articles.

1. Pandemic Restrictions and Spatiotemporal Crime Patterns in New York, São Paulo, and Stockholm

2. Machine Learning

3. Spatiotemporal analysis of burglary in multifamily housing in Fukuoka City, Japan

4. Mining Mobile Phone Data to Investigate Urban Crime Theories at Scale

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Learning context-aware region similarity with effective spatial normalization over Point-of-Interest data;Information Processing & Management;2024-05

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