Predicting Missing Links via Local Information

Details Predicting Missing Links via Local Information Predicting Missing Links via Local Information

2009-01-05

arxiv.org/abs/0901.0553v2

Eur. Phys. J. B 71 (2009) 623-630

Physics

Data Analysis, Statistics and Probability

For International Workshop: "The Physics Approach To Risk: Agent-Based Models and Networks", http://intern.sg.ethz.ch/cost-p10

Scientific paper

10.1140/EPJB/E2009-00335-8

Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.

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