  Link prediction is a fundamental challenge in network science. Among various
methods, local similarity indices are widely used for their high
cost-performance. However, the performance is less robust: for some networks
local indices are highly competitive to state-of-the-art algorithms while for
some other networks they are very poor. Inspired by techniques developed for
recommender systems, we propose an enhancement framework for local indices
based on collaborative filtering (CF). Considering the delicate but important
difference between personalized recommendation and link prediction, we further
propose an improved framework named as self-included collaborative filtering
(SCF). The SCF framework significantly improved the accuracy and robustness of
well-known local indices. The combination of SCF framework and a simple local
index can produce an index with competitive performance and much lower
complexity compared with elaborately-designed state-of-the-art algorithms.
