  With complex networks emerging as an effective tool to tackle
multidisciplinary problems, models of network generation have gained an
importance of their own. These models allow us to extensively analyze the data
obtained from real-world networks, study their relevance and corroborate
theoretical results. In this work, we introduce methods, based on degree
preserving rewiring, that can be used to tune the clustering and
degree-correlations in directed networks with random and scale-free topologies.
They provide null-models to investigate the role of the mentioned properties
along with their strengths and limitations. We find that in the case of
clustering, structural relationships, that are independent of topology and
rewiring schemes are revealed, while in the case of degree-correlations, the
network topology is found to play an important role in the working of the
mechanisms. We also study the effects of link-density on the efficiency of
these rewiring mechanisms and find that in the case of clustering, the topology
of the network plays an important role in determining how link-density affects
the rewiring process, while in the case of degree-correlations, the
link-density and topology, play no role for sufficiently large number of
rewiring steps. Besides the intended purpose of tuning network properties, the
proposed mechanisms can also be used as a tool to reveal structural
relationships and topological constraints.
