Reconstructing propagation networks with natural diversity and identifying hidden sources

Zhesi Shen, Wen-Xu Wang, Ying Fang, Zengru Di, Ying-Cheng Lai

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164 Citations (Scopus)
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Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic
spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems
Original languageEnglish
Article number4323
JournalNature Communications
Publication statusPublished - 11 Jul 2014

Bibliographical note

Date of Acceptance: 06/06/2014

W.-X.W. was supported by NSFC under grant number. 11105011, CNNSF under grant number 61074116 and the Fundamental Research Funds for the Central Universities. Y.-C.L. was supported by AFOSR under grant number FA9550-10-1-0083 and by NSF
under grant number CDI-1026710


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