Significance

Quantifying a network's link predictability allows us to (i) evaluate predictive algorithms associated with the network, (ii) estimate the extent to which the organization of the network is explicable, and (iii) monitor sudden mechanistic changes during the network's evolution. The hypothesis of this paper is that a group of links is predictable if removing them has only a small effect on the network's structural features. We introduce a quantitative index for measuring link predictability and an algorithm that outperforms state-of-the-art link prediction methods in both accuracy and universality. This study provides fundamental insights into  important scientific problems and will aid in the development of information filtering technologies.

Abstract

The organization of real networks usuallyembodies both regularities and irregularities, and, in principle, the formercan be modeled. The extent to which the formation of a network can be explainedcoincides with our ability to predict missing links. To understand networkorganization, we should be able to estimate link predictability. We assume thatthe regularity of a network is reflected in the consistency of structuralfeatures before and after a random removal of a small set of links. Based on theperturbation of the adjacency matrix, we propose a universal structuralconsistency index that is free of prior knowledge of network organization.Extensive experiments on disparate real-world networks demonstrate that (i)structural consistency is a good estimation of link predictability and (ii) aderivative algorithm outperforms state-of-the-art link prediction methods inboth accuracy and robustness. This analysis has further applications inevaluating link prediction algorithms and monitoring sudden changes in evolvingnetwork mechanisms. It will provide unique fundamental insights into theabove-mentioned academic research fields, and will foster the development ofadvanced information filtering technologies of interest to informationtechnology practitioners.

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http://www.pnas.org/content/112/8/2325.full?sid=61535af6-9110-483a-9370-a9389abd7977

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