Numerical evaluation of the upper critical dimension of percolation in scale-free networks

We propose numerical methods to evaluate the upper critical dimension d(c) of random percolation clusters in Erdös-Rényi networks and in scale-free networks with degree distribution P(k) approximately k(-lambda), where k is the degree of a node and lambda is the broadness of the degree distribution. Our results support the theoretical prediction, d(c) = 2(lambda - 1)(lambda - 3) for scale-free networks with 3 < lambda < 4 and d(c) = 6 for Erdös-Rényi networks and scale-free networks with lambda > 4 . When the removal of nodes is not random but targeted on removing the highest degree nodes we obtain d(c) = 6 for all lambda > 2 . Our method also yields a better numerical evaluation of the critical percolation threshold p(c) for scale-free networks. Our results suggest that the finite size effects increases when lambda approaches 3 from above.