Reconstructing propagation networks with natural diversity and identifying hidden sources

Zhesi Shen, Wen-Xu Wang (), Ying Fan, Zengru Di and Ying-Cheng Lai
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Zhesi Shen: School of Systems Science, Beijing Normal University
Wen-Xu Wang: School of Systems Science, Beijing Normal University
Ying Fan: School of Systems Science, Beijing Normal University
Zengru Di: School of Systems Science, Beijing Normal University
Ying-Cheng Lai: School of Electrical, Computer and Energy Engineering, Arizona State University

Nature Communications, 2014, vol. 5, issue 1, 1-10

Abstract: Abstract 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.

Date: 2014
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DOI: 10.1038/ncomms5323

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