Renormalization group on complex networks

In this article, I describe a renormalization group approach for complex networks, as applied by Rozenfeld et.al. in 1. A large number of naturally occuring networks are scale-free, i.e. display a powerlaw degree distribution 2. Moreover, many of these networks display both the small world property, and fractal characteristics. The RG approach demonstrates a transition between fractal and small world networks with increasing scale, and enables the classification of networks in one of the two universality classes. Assuming an underlying fractal structure, it also provides a method for determining the distribution of overlying shortcuts in the network.