  Complex networks have become powerful mechanisms for studying a variety of
realworld systems. Consequently, many human-designed network models are
proposed that reproduce nontrivial properties of complex networks, such as
long-tail degree distribution or high clustering coefficient. Therefore, we may
utilize network models in order to generate graphs similar to desired networks.
However, a desired network structure may deviate from emerging structure of any
generative model, because no selected single model may support all the needed
properties of the target graph and instead, each network model reflects a
subset of the required features. In contrast to the classical approach of
network modeling, an appropriate modern network model should adapt the desired
features of the target network. In this paper, we propose an automatic approach
for constructing network models that are adapted to the desired network
features. We employ Genetic Algorithms in order to evolve network models based
on the characteristics of the target networks. The experimental evaluations
show that our proposed framework, called NetMix, results network models that
outperform baseline models according to the compliance with the desired
features of the target networks.
