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

A parameter free similarity index based on clustering ability for link prediction in complex networks