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Supervised Classification for Link Prediction in Facebook Ego Networks With Anonymized Profile Information

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  • Riccardo Giubilei

    (Sapienza University of Rome
    Luiss Guido Carli)

  • Pierpaolo Brutti

    (Sapienza University of Rome)

Abstract

Social networks are very dynamic objects where nodes and links are continuously added or removed. Hence, an important but challenging task is link prediction, that is, to predict the likelihood of a future association between any two nodes. We use a classification approach to perform link prediction on data retrieved from Facebook in the typical form of ego networks. In addition to the more traditional topological features, we also consider the attributes of the nodes—i.e., users’ publicly available profile information—to fully assess the similarity between nodes. We propose two new attribute-based features, validating their predictive power through an extensive comparison with natural competitors from the literature. Finally, one of the proposed features is selected when building a state-of-the-art procedure for link prediction that achieves an average AUROC of 96.59% over 85 test ego networks. Valuable insights on the interpretation of the results in the specific context of friendship recommendation in Facebook are also provided.

Suggested Citation

  • Riccardo Giubilei & Pierpaolo Brutti, 2022. "Supervised Classification for Link Prediction in Facebook Ego Networks With Anonymized Profile Information," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 302-325, July.
    • Handle: RePEc:spr:jclass:v:39:y:2022:i:2:d:10.1007_s00357-021-09408-2
    DOI: 10.1007/s00357-021-09408-2
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    References listed on IDEAS

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    1. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    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.
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