  The scale-free model of Barabasi and Albert gave rise to a burst of activity
in the field of complex networks. In this paper, we revisit one of the main
assumptions of the model, the preferential attachment rule. We study a model in
which the PA rule is applied to a neighborhood of newly created nodes and thus
no global knowledge of the network is assumed. We numerically show that global
properties of the BA model such as the connectivity distribution and the
average shortest path length are quite robust when there is some degree of
local knowledge. In contrast, other properties such as the clustering
coefficient and degree-degree correlations differ and approach the values
measured for real-world networks.
