Topical issue on The Physics Approach to Risk: Agent-Based Models and Networks

The European Physical Journal B

, Volume 71, Issue 4, pp 623-630

First online:

Predicting missing links via local information

  • Tao ZhouAffiliated withResearch Center for Complex System Science, University of Shanghai for Science and TechnologyDepartment of Physics, University of FribourgDepartment of Modern Physics, University of Science and Technology of China Email author 
  • , Linyuan LüAffiliated withResearch Center for Complex System Science, University of Shanghai for Science and TechnologyDepartment of Physics, University of Fribourg
  • , Yi-Cheng ZhangAffiliated withResearch Center for Complex System Science, University of Shanghai for Science and TechnologyDepartment of Physics, University of FribourgDepartment of Modern Physics, University of Science and Technology of China

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Missing link prediction in 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 Neighbours, has the best overall performance, and the Adamic-Adar index performs 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 neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.

PACS

89.75.-k Complex systems 05.65.+b Self-organized systems