Link prediction applied to an open large-scale online social network
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In this paper, we describe experiments examining the practicality of applying link prediction to an open large-scale online social network. We rely on metrics that are strictly topological, making use of one previously identified metric and one of our own. We directly address the open nature of the network through a study of the linking dynamics over time between users and the effect the openness of the network (i.e. users entering/leaving the network) has on our ability to predict new friendship links. We follow users from the time they enter the network to 10 months after joining and examine the effect of applying link prediction at different points. Analysis shows that prediction results are best shortly after users have entered the network and that the precision and recall of link prediction results diminish the longer users have been members of the network. To the best of our knowledge, our analysis is the most comprehensive in terms of analyzing link prediction in an open large-scale online social network. Copyright 2010 ACM.
author list (cited authors)
Corlette, D., & Shipman, F. M.