  Complex networks have been applied to model numerous interactive nonlinear
systems in the real world. Knowledge about network topology is crucial for
understanding the function, performance and evolution of complex systems. In
the last few years, many network metrics and models have been proposed to
illuminate the network topology, dynamics and evolution. Since these network
metrics and models derive from a wide range of studies, a systematic study is
required to investigate the correlations between 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.
