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Link Prediction in Aligned Heterogeneous Networks

  • Fangbing Liu
  • Shu-Tao Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)

Abstract

Social networks develop rapidly and often contain heterogeneous information. When users join a new social network, recommendation affects their first impressions on this social network. Therefore link prediction for new users is significant. However, due to the lack of sufficient active data of new users in the new social network (target network), link prediction often encounters the cold start problem. In this paper, we attempt to solve the user-user link prediction problem for new users by utilizing data in a similar social network (source network). In order to bridge the two networks, three categories of local features related to single edge and one category of global features associated with multiple edges are selected. The Aligned Factor Graph (AFG) model is proposed for prediction, and Aligned Structure Algorithm is used to reduce the factor graph scale and keep the prediction performance at the same time. Experiments on two real social networks, i.e., Twitter and Foursquare show that AFG model works well when users leave little data in target network.

Keywords

Link prediction Heterogeneous network Aligned factor graph model 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School at ShenzhenTsinghua UniversityBeijingChina
  2. 2.Tsinghua National Laboratory for Information Science and TechnologyBeijingChina

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