  The advent of Internet and World Wide Web has led to unprecedent growth of
the information available. People usually face the information overload by
following a limited number of sources which best fit their interests. It has
thus become important to address issues like who gets followed and how to allow
people to discover new and better information sources. In this paper we conduct
an empirical analysis on different on-line social networking sites, and draw
inspiration from its results to present different source selection strategies
in an adaptive model for social recommendation. We show that local search rules
which enhance the typical topological features of real social communities give
rise to network configurations that are globally optimal. These rules create
networks which are effective in information diffusion and resemble structures
resulting from real social systems.
