FPGN: follower prediction framework for infectious disease prevention

Yu, J; Zhang, X; Wang, H; Wang, X; Zhang, W; Zhang, Y

FPGN: follower prediction framework for infectious disease prevention

Yu, J

Zhang, X Wang, H

Wang, X Zhang, W Zhang, Y

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: World Wide Web, 2023, 26, (6), pp. 3795-3814
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Yu, J https://orcid.org/0000-0002-2032-7727
dc.contributor.author	Zhang, X
dc.contributor.author	Wang, H https://orcid.org/0000-0003-3158-9586
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, W
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638
dc.date.accessioned	2024-03-04T04:05:35Z
dc.date.available	2024-03-04T04:05:35Z
dc.date.issued	2023-11-01
dc.identifier.citation	World Wide Web, 2023, 26, (6), pp. 3795-3814
dc.identifier.issn	1386-145X
dc.identifier.issn	1573-1413
dc.identifier.uri	http://hdl.handle.net/10453/176065
dc.description.abstract	In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on (α , β)-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	World Wide Web
dc.relation.isbasedon	10.1007/s11280-023-01205-8
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	FPGN: follower prediction framework for infectious disease prevention
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-04T04:05:35Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	6

Abstract:

In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on (α , β)-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.

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http://hdl.handle.net/10453/176065