Predictions of missing links of incomplete networks like protein-protein
interaction networks or very likely but not yet existent links in evolutionary
networks like friendship networks in web society can be considered as a
guideline for further experiments or valuable information for web users. In
this paper, we introduce a local path index to estimate the likelihood of the
existence of a link between two nodes. We propose a network model with
controllable density and noise strength in generating links, as well as collect
data of six real networks. Extensive numerical simulations on both modeled
networks and real networks demonstrated the high effectiveness and efficiency
of the local path index compared with two well-known and widely used indices,
the common neighbors and the Katz index. Indeed, the local path index provides
competitively accurate predictions as the Katz index while requires much less
CPU time and memory space, which is therefore a strong candidate for potential
practical applications in data mining of huge-size networks.Comment: 8 pages, 5 figures, 3 table