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Complex networks have been applied to model numerous interactive
nonlinear systems in the real world. Knowledge about network topology
is crucial to an understanding of the function, performance and
evolution of complex systems. In the last few years, many network
metrics and models have been proposed to investigate the network
topology, dynamics and evolution. Since these network metrics and
models are derived from a wide range of studies, a systematic study
is required to investigate the correlations among them. The present
paper explores the effect of degree correlation on the other network
metrics through studying an ensemble of graphs where the degree
sequence (set of degrees) is fixed. We show that to some extent, the
characteristic path length, clustering coefficient, modular extent
and robustness of networks are directly influenced by the degree
correlation. 相似文献
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