An Anti_Turing Test: Reduced Variables for Social Network Friends' Recommendations
This work addresses the issue of how social networks generate friend recommendations, which is incremental as it builds on existing methods like friend-of-a-friend but adds new techniques.
The paper tackles the problem of understanding and reproducing social network friend recommendation lists, including their time-dependent characteristics, by proposing algorithms and an automated software tool that incorporates randomization and interestingness criteria, and demonstrates its use through simulation on actual social network data.
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.