TY - JOUR
T1 - Coherence Scaling of Noisy Second-Order Scale-Free Consensus Networks
AU - Xu, Wanyue
AU - Wu, Bin
AU - Zhang, Zuobai
AU - Zhang, Zhongzhi
AU - Kan, Haibin
AU - Chen, Guanrong
PY - 2022/7
Y1 - 2022/7
N2 - A striking discovery in the field of network science is that the majority of real networked systems have some universal structural properties. In general, they are simultaneously sparse, scale-free, small-world, and loopy. In this article, 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 HSO characterized in terms of the H₂-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 HSO scales sublinearly with the vertex number N. We then study analytically HSO for a class of iteratively growing networks--pseudofractal scale-free webs (PSFWs), and obtain an exact solution to HSO, which also increases sublinearly in N, with an exponent much smaller than 1. To explain the reasons for this sublinear behavior, we finally study HSO for Sierpinśki gaskets, for which HSO grows superlinearly in N, with a power exponent much larger than 1. Sierpinśki gaskets have the same number of vertices and edges as the PSFWs but do not display the scale-free and small-world properties. We thus conclude that the scale-free, small-world, and loopy topologies are jointly responsible for the observed sublinear scaling of HSO.
AB - A striking discovery in the field of network science is that the majority of real networked systems have some universal structural properties. In general, they are simultaneously sparse, scale-free, small-world, and loopy. In this article, 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 HSO characterized in terms of the H₂-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 HSO scales sublinearly with the vertex number N. We then study analytically HSO for a class of iteratively growing networks--pseudofractal scale-free webs (PSFWs), and obtain an exact solution to HSO, which also increases sublinearly in N, with an exponent much smaller than 1. To explain the reasons for this sublinear behavior, we finally study HSO for Sierpinśki gaskets, for which HSO grows superlinearly in N, with a power exponent much larger than 1. Sierpinśki gaskets have the same number of vertices and edges as the PSFWs but do not display the scale-free and small-world properties. We thus conclude that the scale-free, small-world, and loopy topologies are jointly responsible for the observed sublinear scaling of HSO.
KW - Coherence
KW - Distributed average consensus
KW - Eigenvalues and eigenfunctions
KW - Gaussian white noise
KW - Laplace equations
KW - Multi-agent systems
KW - multiagent systems
KW - network coherence
KW - Noise measurement
KW - Resistance
KW - Robustness
KW - scale-free network
KW - small-world network
UR - http://www.scopus.com/inward/record.url?scp=85101746708&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85101746708&origin=recordpage
U2 - 10.1109/TCYB.2021.3052519
DO - 10.1109/TCYB.2021.3052519
M3 - 21_Publication in refereed journal
VL - 52
SP - 5923
EP - 5934
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
SN - 2168-2267
IS - 7
ER -