  The organization in brain networks shows highly modular features with weak
inter-modular interaction. The topology of the networks involves emergence of
modules and sub-modules at different levels of constitution governed by fractal
laws. The modular organization, in terms of modular mass, inter-modular, and
intra-modular interaction, also obeys fractal nature. The parameters which
characterize topological properties of brain networks follow one parameter
scaling theory in all levels of network structure which reveals the
self-similar rules governing the network structure. The calculated fractal
dimensions of brain networks of different species are found to decrease when
one goes from lower to higher level species which implicates the more ordered
and self-organized topography at higher level species. The sparsely distributed
hubs in brain networks may be most influencing nodes but their absence may not
cause network breakdown, and centrality parameters characterizing them also
follow one parameter scaling law indicating self-similar roles of these hubs at
different levels of organization in brain networks.
