Multi-source localization on complex networks with limited observers

Fu, Ling; Shen, Zhesi; Wang, Wen-Xu; Fan, Ying; Di, Zengru

doi:10.1209/0295-5075/113/18006

Cited by 40 publications

(31 citation statements)

References 20 publications

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“…We also study the case of multiple sources and propose a Source Candidate Clustering and Estimation (SCCE) algorithm, which makes less assumptions compared with the work [34] as we do not assume that infection sources have the same diffusion parameters, and the number of sources is also unknown a priori. SCCE can be divided into two steps.…”

Section: B Our Contributionsmentioning

confidence: 99%

Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

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We study the problem of identifying infection sources in a network based on the network topology, and a subset of infection timestamps. In the case of a single infection source in a tree network, we derive the maximum likelihood estimator of the source and the unknown diffusion parameters. We then introduce a new heuristic involving an optimization over a parametrized family of Gromov matrices to develop a single source estimation algorithm for general graphs. Compared with the breadth-first search tree heuristic commonly adopted in the literature, simulations demonstrate that our approach achieves better estimation accuracy than several other benchmark algorithms, even though these require more information like the diffusion parameters. We next develop a multiple sources estimation algorithm for general graphs, which first partitions the graph into source candidate clusters, and then applies our single source estimation algorithm to each cluster. We show that if the graph is a tree, then each source candidate cluster contains at least one source. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithms are able to estimate the true infection source(s) to within a small number of hops with a small portion of the infection timestamps being observed.

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Section: B Our Contributionsmentioning

confidence: 99%

Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

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“…Recently, Shen et al [21] developed a time-reversal backward spreading algorithm to efficiently locate the source of a diffusion-like process and proposed a general locatability condition. For multiple sources detection, Fu et al [22] investigated a maximum-minimum strategy based on backward diffusion, which has been further extended by Hu et al [23] via integer programming.…”

Section: Introductionmentioning

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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“…Nino Antulov-Fantulin et al [21] proposed a new source localization method based Monte-Carlo simulation under the SIR model. Fu et al [22] studied a backward diffusion-based source localization method. Based on the times at which the diffusion reached partial observers, the maximum time when the diffusion goes reversely from partial observers to each node is calculated.…”

Section: Introductionmentioning

confidence: 99%

Locating Multiple Sources of Contagion in Complex Networks under the SIR Model

Liu

Zhao

et al. 2019

Applied Sciences

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Simultaneous outbreaks of contagion are a great threat against human life, resulting in great panic in society. It is urgent for us to find an efficient multiple sources localization method with the aim of studying its pathogenic mechanism and minimizing its harm. However, our ability to locate multiple sources is strictly limited by incomplete information about nodes and the inescapable randomness of the propagation process. In this paper, we present a valid approach, namely the Potential Concentration Label method, which helps locate multiple sources of contagion faster and more accurately in complex networks under the SIR(Susceptible-Infected-Recovered) model. Through label assignment in each node, our aim is to find the nodes with maximal value after several iterations. The experiments demonstrate that the accuracy of our multiple sources localization method is high enough. With the number of sources increasing, the accuracy of our method declines gradually. However, the accuracy remains at a slight fluctuation when average degree and network scale make a change. Moreover, our method still keeps a high multiple sources localization accuracy with noise of various intensities, which shows its strong anti-noise ability. I believe that our method provides a new perspective for accurate and fast multi-sources localization in complex networks.

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Multi-source localization on complex networks with limited observers

Cited by 40 publications

References 20 publications

Estimating Infection Sources in Networks Using Partial Timestamps

Estimating Infection Sources in Networks Using Partial Timestamps

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

Locating Multiple Sources of Contagion in Complex Networks under the SIR Model

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