Learning to Infer Social Ties in Large Networks

  • Wenbin Tang
  • Honglei Zhuang
  • Jie Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)


In online social networks, most relationships are lack of meaning labels (e.g., “colleague” and “intimate friends”), simply because users do not take the time to label them. An interesting question is: can we automatically infer the type of social relationships in a large network? what are the fundamental factors that imply the type of social relationships? In this work, we formalize the problem of social relationship learning into a semi-supervised framework, and propose a Partially-labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social ties. We tested the model on three different genres of data sets: Publication, Email and Mobile. Experimental results demonstrate that the proposed PLP-FGM model can accurately infer 92.7% of advisor-advisee relationships from the coauthor network (Publication), 88.0% of manager-subordinate relationships from the email network (Email), and 83.1% of the friendships from the mobile network (Mobile). Finally, we develop a distributed learning algorithm to scale up the model to real large networks.


Online Social Network Link Prediction Slave Node Publication Network Coauthor Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wenbin Tang
    • 1
  • Honglei Zhuang
    • 1
  • Jie Tang
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityChina

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