Coherence Scaling of Noisy Second-Order Scale-Free Consensus Networks
Wanyue Xu, Bin Wu, Zuobai Zhang, Zhongzhi Zhang, Haibin Kan and, Guanrong Chen

TL;DR
This paper investigates how the network coherence in noisy second-order scale-free networks scales with size, revealing sublinear growth due to their universal structural properties, and contrasting it with non-scale-free networks.
Contribution
It provides the first analytical solution for network coherence in scale-free webs and links structural properties to coherence scaling behavior.
Findings
Real-world networks exhibit sublinear coherence scaling.
Analytical solution for coherence in pseudofractal scale-free webs.
Superlinear coherence growth in non-scale-free Sierpiński gaskets.
Abstract
A striking discovery in the field of network science is that the majority of real networked systems have some universal structural properties. In generally, they are simultaneously sparse, scale-free, small-world, and loopy. In this paper, we investigate the second-order consensus of dynamic networks with such universal structures subject to white noise at vertices. We focus on the network coherence characterized in terms of the -norm of the vertex systems, which measures the mean deviation of vertex states from their average value. We first study numerically the coherence of some representative real-world networks. We find that their coherence scales sublinearly with the vertex number . We then study analytically for a class of iteratively growing networks -- pseudofractal scale-free webs (PSFWs), and obtain an exact solution to…
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