  We propose a growing network model that consists of two tunable mechanisms:
growth by merging modules which are represented as complete graphs and a
fitness-driven preferential attachment. Our model exhibits the three prominent
statistical properties are widely shared in real biological networks, for
example gene regulatory, protein-protein interaction, and metabolic networks.
They retain three power law relationships, such as the power laws of degree
distribution, clustering spectrum, and degree-degree correlation corresponding
to scale-free connectivity, hierarchical modularity, and disassortativity,
respectively. After making comparisons of these properties between model
networks and biological networks, we confirmed that our model has inference
potential for evolutionary processes of biological networks.
