  Link prediction is the problem of inferring whether potential edges between
pairs of vertices in a graph will be present or absent in the near future. To
perform this task it is usual to use information provided by a number of
available and observed vertices/edges. Then, a number of edge scoring methods
based on this information can be created. Usually, these methods assess local
structures of the observed graph, assuming that closer vertices in the original
period of observation will be more likely to form a link in the future. In this
paper we explore the combination of local and global features to conduct link
prediction in online social networks. The contributions of the paper are
twofold: a) We evaluate a number of strategies that combines global and local
features tackling the locality assumption of link prediction scoring methods,
and b) We only use network topology-based features, avoiding the inclusion of
informational or transactional based features that involve heavy computational
costs in the methods. We evaluate our proposal using real-world data provided
by Skout Inc., an affinity online social network with millions of users around
the world. Our results show that our proposal is feasible.
