You Are Who Knows You: Predicting Links Between Non-members of Facebook

  • Emöke-Ágnes HorvátEmail author
  • Michael Hanselmann
  • Fred A. Hamprecht
  • Katharina A. Zweig
Part of the Springer Proceedings in Complexity book series (SPCOM)


Could online social networks like Facebook be used to infer relationships between non-members? We show that the combination of relationships between members and their e-mail contacts to non-members provides enough information to deduce a substantial proportion of the relationships between non-members. Using structural features we are able to predict relationship patterns that are stable over independent social networks of the same type. Our findings are not specific to Facebook and can be applied to other platforms involving online invitations.


Online social network Privacy Link prediction Machine learning Random forest classifier 



E.Á. Horvát and K.A. Zweig were supported by the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences, University of Heidelberg, Germany, which is funded by the German Excellence Initiative (GSC 220). F.A. Hamprecht and K.A. Zweig were supported by a fellowship of the Marsilius Kolleg, University of Heidelberg, Germany.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Emöke-Ágnes Horvát
    • 1
    • 3
    Email author
  • Michael Hanselmann
    • 1
  • Fred A. Hamprecht
    • 1
    • 2
  • Katharina A. Zweig
    • 2
    • 3
  1. 1.Interdisciplinary Center for Scientific Computing (IWR), Heidelberg Collaboratory for Image Processing (HCI)University of HeidelbergHeidelbergGermany
  2. 2.Marsilius KollegUniversity of HeidelbergHeidelbergGermany
  3. 3.Technical University of KaiserslauternKaiserslauternGermany

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