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Title: Recovering network topologies via Taylor expansion and compressive sensing

Gaining knowledge of the intrinsic topology of a complex dynamical network is the precondition to understand its evolutionary mechanisms and to control its dynamical and functional behaviors. In this article, a general framework is developed to recover topologies of complex networks with completely unknown node dynamics based on Taylor expansion and compressive sensing. Numerical simulations illustrate the feasibility and effectiveness of the proposed method. Moreover, this method is found to have good robustness to weak stochastic perturbations. Finally, the impact of two major factors on the topology identification performance is evaluated. This method provides a natural and direct point to reconstruct network topologies from measurable data, which is likely to have potential applicability in a wide range of fields.
Authors:
;  [1] ; ;  [2] ;  [3]
  1. Computer School, Wuhan University, Hubei 430072 (China)
  2. School of Mathematics and Statistics, Wuhan University, Hubei 430072 (China)
  3. Global Navigation Satellite System Research Center, Wuhan University, Hubei 430072 (China)
Publication Date:
OSTI Identifier:
22402547
Resource Type:
Journal Article
Resource Relation:
Journal Name: Chaos (Woodbury, N. Y.); Journal Volume: 25; Journal Issue: 4; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; COMPUTERIZED SIMULATION; DISTURBANCES; DYNAMICS; NETWORK ANALYSIS; SERIES EXPANSION; STOCHASTIC PROCESSES; TOPOLOGY