Multiple sources identification in networks with partial timestamps

Tang, Wenchang; Ji, Feng; Tay, Wee Peng

doi:10.1109/globalsip.2017.8309037

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Compared with the single source localization problem, the case of multiple source localization is far more complicated because it is a combinatorial optimization problem. Several studies [36][37][38] have addressed the problem via a two-step strategy that first obtains a set of source candidate clusters and then applies single source localization algorithms to identify the source in each cluster. However, a general framework for simultaneously determining the number of sources and their locations in a large complex network is still lacking.…”

Section: Discussionmentioning

confidence: 99%

Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Wang

Zhao

et al. 2019

Applied Sciences

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Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

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Section: Discussionmentioning

confidence: 99%

Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Wang

Zhao

et al. 2019

Applied Sciences

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“…Compared with the single-source localization problem, the case of multiple sources localization becomes more difficult because it needs to locate the sources at the same time in most cases. However, there are some works [6,28,29] dealing with multi-source localization via two-step strategies that first divide the network into several communities and then apply the single-source localization method to locate sources in each community. In addition, the network topology we consider in this paper is static and just single layer.…”

Section: Discussionmentioning

confidence: 99%

Locating the Epidemic Source in Complex Networks with Sparse Observers

Wang

Zhao

et al. 2019

Applied Sciences

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Epidemic source localization is one of the most meaningful areas of research in complex networks, which helps solve the problem of infectious disease spread. Limited by incomplete information of nodes and inevitable randomness of the spread process, locating the epidemic source becomes a little difficult. In this paper, we propose an efficient algorithm via Bayesian Estimation to locate the epidemic source and find the initial time in complex networks with sparse observers. By modeling the infected time of observers, we put forward a valid epidemic source localization method for tree network and further extend it to the general network via maximum spanning tree. The numerical analyses in synthetic networks and empirical networks show that our algorithm has a higher source localization accuracy than other comparison algorithms. In particular, when the randomness of the spread path enhances, our algorithm has a better performance. We believe that our method can provide an effective reference for epidemic spread and source localization in complex networks.

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Research on Cascading Fault Location of Chemical Material Networks Based on BFS-Time-Reversal Backpropagation Algorithm

Wang

Liu

et al. 2022

Arab J Sci Eng

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Multiple sources identification in networks with partial timestamps

Cited by 5 publications

References 25 publications

Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Locating the Epidemic Source in Complex Networks with Sparse Observers

Research on Cascading Fault Location of Chemical Material Networks Based on BFS-Time-Reversal Backpropagation Algorithm

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