Consistency between functional and structural networks of coupled nonlinear oscillators.
In data-based reconstruction of complex networks, dynamical information can be measured and exploited to generate a functional network, but is it a true representation of the actual (structural) network? That is, when do the functional and structural networks match and is a perfect matching possible? To address these questions, we use coupled nonlinear oscillator networks and investigate the transition in the synchronization dynamics to identify the conditions under which the functional and structural networks are best matched. We find that, as the coupling strength is increased in the weak-coupling regime, the consistency between the two networks first increases and then decreases, reaching maximum in an optimal coupling regime. Moreover, by changing the network structure, we find that both the optimal regime and the maximum consistency will be affected. In particular, the consistency for heterogeneous networks is generally weaker than that for homogeneous networks. Based on the stability of the functional network, we propose further an efficient method to identify the optimal coupling regime in realistic situations where the detailed information about the network structure, such as the network size and the number of edges, is not available. Two real-world examples are given: corticocortical network of cat brain and the Nepal power grid. Our results provide new insights not only into the fundamental interplay between network structure and dynamics but also into the development of methodologies to reconstruct complex networks from data.
Department of Physics, Zhejiang University, Hangzhou 310027, China.|School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.
Weijie
W
Lin
School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.|Institute of Theoretical & Computational Physics, Shaanxi Normal University, Xi'an 710062, China.
Yafeng
Y
Wang
Department of Physics, Zhejiang University, Hangzhou 310027, China.
Heping
H
Ying
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Ying-Cheng
YC
Lai
School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.|Institute of Theoretical & Computational Physics, Shaanxi Normal University, Xi'an 710062, China.
Xingang
X
Wang