  An important fact in studying the link prediction is that the structural
properties of networks have significant impacts on the performance of
algorithms. Therefore, how to improve the performance of link prediction with
the aid of structural properties of networks is an essential problem. By
analyzing many real networks, we find a common structure property: nodes are
preferentially linked to the nodes with the weak clique structure (abbreviated
as PWCS to simplify descriptions). Based on this PWCS phenomenon, we propose a
local friend recommendation (FR) index to facilitate link prediction. Our
experiments show that the performance of FR index is generally better than some
famous local similarity indices, such as Common Neighbor (CN) index,
Adamic-Adar (AA) index and Resource Allocation (RA) index. We then explain why
PWCS can give rise to the better performance of FR index in link prediction.
Finally, a mixed friend recommendation index (labelled MFR) is proposed by
utilizing the PWCS phenomenon, which further improves the accuracy of link
prediction.
