  Complex networks from such different fields as biology, technology or
sociology share similar organization principles. The possibility of a unique
growth mechanism promises to uncover universal origins of collective behaviour.
In particular, the emergence of self-similarity in complex networks raises the
fundamental question of the growth process according to which these structures
evolve. Here we investigate the concept of renormalization as a mechanism for
the growth of fractal and non-fractal modular networks. We show that the key
principle that gives rise to the fractal architecture of networks is a strong
effective 'repulsion' (or, disassortativity) between the most connected nodes
(that is, the hubs) on all length scales, rendering them very dispersed. More
importantly, we show that a robust network comprising functional modules, such
as a cellular network, necessitates a fractal topology, suggestive of an
evolutionary drive for their existence.
