Enhancing the synchronizability of networks by rewiring based on tabu search and a local greedy algorithm

Abstract

By considering the eigenratio of the Laplacian matrix as the synchronizability measure, this paper presents an efficient method to enhance the synchronizability of undirected and unweighted networks via rewiring. The rewiring method combines the use of tabu search and a local greedy algorithm so that an effective search of solutions can be achieved. As demonstrated in the simulation results, the performance of the proposed approach outperforms the existing methods for a large variety of initial networks, both in terms of speed and quality of solutions.

Adaptive function projective synchronization of uncertain complex dynamical networks with disturbance

“…[1][2][3][4][5][6][7][8][9] As a typical kind of dynamics, synchronization in complex networks has received a great deal of attention, and many types of synchronization phenomena have been reported, such as complete synchronization, anti-synchronization, phase synchronization, lag synchronization, intermittent lag synchronization, generalized synchronization, intermittent generalized synchronization, time scale synchronization, and projective synchronization. [10][11][12][13][14][15][16][17][18][19][20][21][22] Projective synchronization (PS) means that the drive and response systems can be synchronized up to a scaling factor. PS was first reported by Mainieri and Rehacek [23] in partially linear systems.…”

Hash function construction using weighted complex dynamical networks

“…In some research results about complex dynamical networks, [24,25] the topology of a network often plays a crucial role in determining its dynamical behaviors, which include the synchronization of the network, the propagation of epidemic and rumor, and the diffusion of fads. [26][27][28][29][30][31][32][33][34] For example, the ability to achieve synchronization in a large-sized nearest-neighbor coupled network can be greatly enhanced by simply adding a tiny fraction of distant links, thereby making the network become a small-world model. [26] Compared with information propagation over other networks (e.g., random networks, small-world networks or scale-free networks), the spreading speed on the nearest neighbor coupled networks is slowest.…”