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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Shen Z,Wang WX,Fan Y,Di Z,Lai YCdoi
10.1038/ncomms5323subject
Has Abstractpub_date
2014-07-11 00:00:00pages
4323issn
2041-1723pii
ncomms5323journal_volume
5pub_type
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