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Research on Link Prediction Features on Location-based Social Networks

  

  1. College of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2015-01-04 Online:2015-04-27 Published:2015-04-29

Abstract:

In addition to friendship information in the traditional social network, LBSN records users’ check-in information, which connects the virtual network and real
life. Combined with the traditional link prediction methods based on network structure and location similarity, we propose two kinds of link prediction features based on users’
check-in time and frequency, prove to be effective by the statistical analysis of the Brightkite dataset, and establish a LBSN link prediction framework with several kinds of
features. The experimental results show that these two types of link prediction features improved the prediction precision.

Key words:  data mining, link prediction, LBSN, node similarity