Random Sequential Renormalization and Agglomerative Percolation in   Networks: Application to Erd"os-R'enyi and Scale-free Graphs
  We study the statistical behavior under random sequential renormalization(RSR) of several network models including Erd"os R'enyi (ER) graphs, scale-free networks and an annealed model (AM) related to ER graphs. In RSR the network is locally coarse grained by choosing at each renormalization step a node at random and joining it to all its neighbors. Compared to previous (quasi-)parallel renormalization methods [C.Song et.al], RSR allows a more fine-grained analysis of the renormalization group (RG) flow, and unravels new features, that were not discussed in the previous analyses. In particular we find that all networks exhibit a second order transition in their RG flow. This phase transition is associated with the emergence of a giant hub and can be viewed as a new variant of percolation, called agglomerative percolation. We claim that this transition exists also in previous graph renormalization schemes and explains some of the scaling laws seen there. For critical trees it happens as N/N0 -> 0 in the limit of large systems (where N0 is the initial size of the graph and N its size at a given RSR step). In contrast, it happens at finite N/N0 in sparse ER graphs and in the annealed model, while it happens for N/N0 -> 1 on scale-free networks. Critical exponents seem to depend on the type of the graph but not on the average degree and obey usual scaling relations for percolation phenomena. For the annealed model they agree with the exponents obtained from a mean-field theory. At late times, the networks exhibit a star-like structure in agreement with the results of Radicchi et. al. While degree distributions are of main interest when regarding the scheme as network renormalization, mass distributions (which are more relevant when considering 'supernodes' as clusters) are much easier to study using the fast Newman-Ziff algorithm for percolation, allowing us to obtain very high statistics. 
Golnoosh Bizhani, Peter Grassberger and Maya Paczuski
cond-mat.stat-mech cs.SI physics.soc-ph