Global Reconstruction of Complex Network Topology via Structured Compressive Sensing

Complex dynamic network is a representative model for the interactions of complex system, such as the Internet network, smart grid, and biological network. Many studies have investigated the dynamics in complex networks and control of complex networks. Among these works, an accurate topology of the complex network is an essential prerequisite. Therefore, reconstruction of the complex network topology from measured node dynamics data is important yet challenging. By analyzing and extracting the underlying feature of unweighted and undirected networks, we propose a structured compressive sensing method that reconstructs the topology of complex network globally. Through intensive numerical simulations of an artificial small-world network, an artificial scale-free network, and two real networks, we find that the proposed method is efficient for complex network topology reconstruction, and it is also robust against weak stochastic perturbations.