SIMULATION OF ISING MODEL ON UNDIRECTED NONLINEAR BARABASI-ALBERT NETWORKS

In the standard Barabasi-Albert growth of a scale-free network, a new node n selects m neighbors from among the earlier added nodes j, with a probability proportional to the number kj of neighborswhich the candidate j has at that time. If the probability instead is proportional to k� j we have a nonlinear Barabasi-Albert network (Onody and de Castro 2004). Now we put Ising spins, up or down, on all nodes of the �nal network and check for ferromagnetic Curie temperatures, as a function of �, m and network size N.