title: Reverse engineering of complex dynamical networks in the presence of time-delayed interactions based on noisy time series.
abstract: Reverse engineering of complex dynamical networks is important for a variety of fields where uncovering the full topology of unknown networks and estimating parameters characterizing the network structure and dynamical processes are of interest. We consider complex oscillator networks with time-delayed interactions in a noisy environment, and develop an effective method to infer the full topology of the network and evaluate the amount of time delay based solely on noise-contaminated time series. In particular, we develop an analytic theory establishing that the dynamical correlation matrix, which can be constructed purely from time series, can be manipulated to yield both the network topology and the amount of time delay simultaneously. Extensive numerical support is provided to validate the method. While our method provides a viable solution to the network inverse problem, significant difficulties, limitations, and challenges still remain, and these are discussed thoroughly.
authors.affiliation: Department of Systems Science, School of Management and Center for Complexity Research, Beijing Normal University, Beijing 100875, China.
authors.forename: Wen-Xu
authors.initials: WX
authors.lastname: Wang
authors.forename: Jie
authors.initials: J
authors.lastname: Ren
authors.forename: Ying-Cheng
authors.initials: YC
authors.lastname: Lai
authors.forename: Baowen
authors.initials: B
authors.lastname: Li