Scale-free random graphs and Potts model

We introduce a simple algorithm that constructs scale-free random graphs efficiently: each vertex i has a prescribed weight Pi proportional to i(-mu) (0 < mu < 1) and an edge can connect vertices i and j with rate PiPj. Corresponding equilibrium ensemble is identified and the problem is solved by the q -> 1 limit of the q-state Potts model with inhomogeneous interactions for all pairs of spins. The number of loops as well as the giant cluster size and the mean cluster size are obtained in the thermodynamic limit as a function of the edge density. Various critical exponents associated with the percolation transition are also obtained together with finite-size scaling forms. The process of forming the giant cluster is qualitatively different between the cases of lambda > 3 and 2 < lambda < 3, where lambda = 1 + mu(-1) is the degree distribution exponent. While for the former, the giant cluster forms abruptly at the percolation transition, for the latter, however, the formation of the giant cluster is gradual and the mean cluster size for finite N shows double peaks.