Coarse Graining for Synchronization in Directed Networks

Abstract

Coarse graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve the same statistical properties as well as the dynamic behaviors as the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse graining in directed networks lacks of consideration. In this paper, we proposed a Topology-aware Coarse Graining (TCG) method to coarse grain the directed networks. Performing the linear stability analysis of synchronization and numerical simulation of the Kuramoto model on four kinds of directed networks, including tree-like networks and variants of Barabási-Albert networks, Watts-Strogatz networks and Erdös-Rényi networks, we find our method can effectively preserve the network synchronizability.

9 pages, 7 figures

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• Self-similar scaling of density in complex real-world networks
• Information filtering via hybridization of similarity preferential diffusion processes
• Reconstructing directed networks for better synchronization
• Network Renormalization
• Spectral coarse graining for random walk in bipartite networks
• Enhancing synchronization in growing networks