  Link prediction in complex network based on solely topological information is
a challenging problem. In this paper, we propose a novel similarity index,
which is efficient and parameter free, based on clustering ability. Here
clustering ability is defined as average clustering coefficient of nodes with
the same degree. The motivation of our idea is that common-neighbors are able
to contribute to the likelihood of forming a link because they own some ability
of clustering their neighbors together, and then clustering ability defined
here is a measure for this capacity. Experimental numerical simulations on both
real-world networks and modeled networks demonstrated the high accuracy and
high efficiency of the new similarity index compared with three well-known
common-neighbor based similarity indices: CN, AA and RA.
