SIMULATION OF ISING MODEL ON UNDIRECTED NONLINEAR BARABÁSI-ALBERT NETWORKS

In the standard Barabási-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 neighbors which the candidate j has at that time. If the probability instead is proportional to we have a nonlinear Barabási-Albert network (Onody and de Castro 2004). Now we put Ising spins, up or down, on all nodes of the final network and check for ferromagnetic Curie temperatures, as a function of α, m and network size N.