Topology reconstruction of complex networks with time-varying parameters nodes

Complex networks with time-varying parameters nodes are of considerable interest in many areas of science, engineering and nature. Reconstructing networks with unknown but bounded time-varying parameters nodes from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient, feasible, and completely data-driven approach to predicting the structures of the networks with unknown time-varying parameters nodes in present of noise or without noise. In particular, through the numerical simulations, we demonstrate that, networks structures can be fully reconstructed even only few information available under the conditions of the systemic parameter is time-varying and in the presence of noise, and this method is effective and robust. We expect our method to be useful in addressing issues of significantly current concern in the information era and natural networks.