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One Plus One Makes Three (for Social Networks)

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  • Emöke-Ágnes Horvát
  • Michael Hanselmann
  • Fred A Hamprecht
  • Katharina A Zweig

Abstract

Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email contacts to non-members on the other hand provides enough information to deduce a substantial proportion of relationships between non-members. Using machine learning we achieve an area under the (receiver operating characteristic) curve () of at least for predicting whether two non-members known by the same member are connected or not, even for conservative estimates of the overall proportion of members, and the proportion of members disclosing their contacts.

Suggested Citation

  • Emöke-Ágnes Horvát & Michael Hanselmann & Fred A Hamprecht & Katharina A Zweig, 2012. "One Plus One Makes Three (for Social Networks)," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
    • Handle: RePEc:plo:pone00:0034740
    DOI: 10.1371/journal.pone.0034740
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    References listed on IDEAS

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    1. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    2. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    3. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    4. Sid Redner, 2008. "Teasing out the missing links," Nature, Nature, vol. 453(7191), pages 47-48, May.
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    Cited by:

    1. Sætra, Henrik Skaug, 2020. "Privacy as an aggregate public good," Technology in Society, Elsevier, vol. 63(C).
    2. Philipp K. Masur, 2020. "How Online Privacy Literacy Supports Self-Data Protection and Self-Determination in the Age of Information," Media and Communication, Cogitatio Press, vol. 8(2), pages 258-269.
    3. Zexun Chen & Sean Kelty & Alexandre G. Evsukoff & Brooke Foucault Welles & James Bagrow & Ronaldo Menezes & Gourab Ghoshal, 2022. "Contrasting social and non-social sources of predictability in human mobility," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Sætra, Henrik Skaug, 2019. "Freedom under the gaze of Big Brother: Preparing the grounds for a liberal defence of privacy in the era of Big Data," Technology in Society, Elsevier, vol. 58(C).

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