Effective feature reduction for link prediction in location-based social networks

Bayrak A. E. , Polat F.

JOURNAL OF INFORMATION SCIENCE, vol.45, pp.676-690, 2019 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45
  • Publication Date: 2019
  • Doi Number: 10.1177/0165551518808200
  • Journal Indexes: Science Citation Index Expanded, Social Sciences Citation Index, Scopus
  • Page Numbers: pp.676-690
  • Keywords: Link prediction, location-based social networks, social networks, MUTUAL INFORMATION, FEATURE-SELECTION, RELEVANCE


In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, common friend detail and place category information of check-in data in order to make use of information in the data which cannot be utilised by the existing features from the literature. We proposed a feature selection method to determine a feature subset that enhances the prediction performance with the removal of redundant features by clustering them. After clustering features, a genetic algorithm is used to determine the ones to select from each cluster. A non-monotonic and feasible feature selection is ensured by the proposed genetic algorithm. Results depict that both new features and the proposed feature selection method improved link prediction performance for LBSNs.