  We present a new model of the evolutionary dynamics and the growth of on-line
social networks. The model emulates people's strategies for acquiring
information in social networks, emphasising the local subjective view of an
individual and what kind of information the individual can acquire when
arriving in a new social context. The model proceeds through two phases: (a) a
discovery phase, in which the individual becomes aware of the surrounding world
and (b) an elaboration phase, in which the individual elaborates locally the
information trough a cognitive-inspired algorithm. Model generated networks
reproduce main features of both theoretical and real-world networks, such as
high clustering coefficient, low characteristic path length, strong division in
communities, and variability of degree distributions.
