  A routine activity of social networks servers is to recommend candidate
friends that one may know and stimulate addition of these people to one's
contacts. An intriguing issue is how these recommendation lists are composed.
This work investigates the main variables involved in the recommendation
activity, in order to reproduce these lists including its time dependent
characteristics. We propose relevant algorithms. Besides conventional
approaches, such as friend_of_a_friend, two techniques of importance have not
been emphasized in previous works: randomization and direct use of
interestingness criteria. An automatic software tool to implement these
techniques is proposed. Its architecture and implementation are discussed.
After a preliminary analysis of actual data collected from social networks, the
tool is used to simulate social network friends' recommendations.
