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Open AccessArticle
Infodemic Source Detection with Information Flow: Foundations and Scalable Computation †
1
Department of Computer Science, City University of Hong Kong, Hong Kong, China
2
Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518055, China
3
College of Computing and Data Science, Nanyang Technological University, Singapore 639978, Singapore
*
Authors to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Zhao, C.; Wang, Z.; Zhou, Q.; Tan, C.W.; Chan, C. Infodemic Source Detection: Enhanced Formulations with Information Flow. In Proceedings of the 2024 IEEE International Symposium on Information Theory (ISIT), Athens, Greece, 7–12 July 2024
Entropy 2025, 27(9), 936; https://doi.org/10.3390/e27090936 (registering DOI)
Submission received: 9 July 2025
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Revised: 11 August 2025
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Accepted: 5 September 2025
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Published: 6 September 2025
Abstract
We consider the problem of identifying the source of a rumor in a network, given only a snapshot observation of infected nodes after the rumor has spread. Classical approaches, such as the maximum likelihood (ML) and joint maximum likelihood (JML) estimators based on the conventional Susceptible–Infectious (SI) model, exhibit degeneracy, failing to uniquely identify the source even in simple network structures. To address these limitations, we propose a generalized estimator that incorporates independent random observation times. To capture the structure of information flow beyond graphs, our formulations consider rate constraints on the rumor and the multicast capacities for cyclic polylinking networks. Furthermore, we develop forward elimination and backward search algorithms for rate-constrained source detection and validate their effectiveness and scalability through comprehensive simulations. Our study establishes a rigorous and scalable foundation on the infodemic source detection.
Share and Cite
MDPI and ACS Style
Wang, Z.; Zhao, C.; Zhou, Q.; Tan, C.W.; Chan, C.
Infodemic Source Detection with Information Flow: Foundations and Scalable Computation. Entropy 2025, 27, 936.
https://doi.org/10.3390/e27090936
AMA Style
Wang Z, Zhao C, Zhou Q, Tan CW, Chan C.
Infodemic Source Detection with Information Flow: Foundations and Scalable Computation. Entropy. 2025; 27(9):936.
https://doi.org/10.3390/e27090936
Chicago/Turabian Style
Wang, Zimeng, Chao Zhao, Qiaoqiao Zhou, Chee Wei Tan, and Chung Chan.
2025. "Infodemic Source Detection with Information Flow: Foundations and Scalable Computation" Entropy 27, no. 9: 936.
https://doi.org/10.3390/e27090936
APA Style
Wang, Z., Zhao, C., Zhou, Q., Tan, C. W., & Chan, C.
(2025). Infodemic Source Detection with Information Flow: Foundations and Scalable Computation. Entropy, 27(9), 936.
https://doi.org/10.3390/e27090936
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