Reconstructing propagation networks with natural diversity and identifying hidden sources.

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 inaccessib

SUBMITTER: Shen Z 

PROVIDER: S-EPMC4104449 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature