Time-series-based prediction of complex oscillator networks via compressive sensing

Wen Xu Wang, Rui Yang, Ying-Cheng Lai, Vassilios Kovanis, Mary Ann F Harrison

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


Complex dynamical networks consisting of a large number of interacting units are ubiquitous in nature and society. There are situations where the interactions in a network of interest are unknown and one wishes to reconstruct the full topology of the network through measured time series. We present a general method based on compressive sensing. In particular, by using power series expansions to arbitrary order, we demonstrate that the network-reconstruction problem can be casted into the form X=Ga, where the vector X and matrix G are determined by the time series and a is a sparse vector to be estimated that contains all nonzero power series coefficients in the mathematical functions of all existing couplings among the nodes. Since a is sparse, it can be solved by the standard L1-norm technique in compressive sensing. The main advantages of our approach include sparse data requirement and broad applicability to a variety of complex networked dynamical systems, and these are illustrated by concrete examples of model and real-world complex networks.

Original languageEnglish (US)
Article number48006
Issue number4
StatePublished - May 2011

ASJC Scopus subject areas

  • General Physics and Astronomy


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