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Large-Scale Parallel Matching of Social Network Profiles

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  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 542))

Abstract

A profile matching algorithm takes as input a user profile of one social network and returns, if existing, the profile of the same person in another social network. Such methods have immediate applications in Internet marketing, search, security, and a number of other domains, which is why this topic saw a recent surge in popularity.

In this paper, we present a user identity resolution approach that uses minimal supervision and achieves a precision of 0.98 at a recall of 0.54. Furthermore, the method is computationally efficient and easily parallelizable. We show that the method can be used to match Facebook, the most popular social network globally, with VKontakte, the most popular social network among Russian-speaking users.

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Notes

  1. 1.

    http://www.facebook.com.

  2. 2.

    http://www.twitter.com.

  3. 3.

    http://www.linkedin.com.

  4. 4.

    http://www.vk.com.

  5. 5.

    https://www.android.com.

  6. 6.

    https://github.com/dmitrib/sn-profile-matching.

  7. 7.

    https://vk.com/dev/users.get.

  8. 8.

    https://developers.facebook.com/docs/graph-api.

  9. 9.

    https://github.com/dmitrib/sn-profile-matching.

  10. 10.

    http://earth-info.nga.mil/gns/html/romanization.html.

  11. 11.

    http://hadoop.apache.org.

  12. 12.

    org.apache.lucene.util.automaton.LevenshteinAutomata.

  13. 13.

    org.apache.lucene.search.spell.LevensteinDistance.

  14. 14.

    http://aws.amazon.com.

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Acknowledgements

This research was conducted as part of a project funded by Digital Society Laboratory LLC. We thank Prof. Chris Biemann and three anonymous reviewers for their thorough comments that significantly improved quality of this paper.

Author information

Authors and Affiliations

  1. FG Language Technology, TU Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  2. Tinkoff Credit Systems Inc., Moscow, Russia

    Dmitry Babaev

  3. National Research University Higher School of Economics, Moscow, Russia

    Sergei Obiedkov

Authors
  1. Alexander Panchenko
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  2. Dmitry Babaev
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  3. Sergei Obiedkov
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Corresponding author

Correspondence to Alexander Panchenko .

Editor information

Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  2. Wolverhampton, United Kingdom

    Natalia Konstantinova

  3. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  4. National Research University Higher School of Economics, Moscow, Russia

    Dmitry Ignatov

  5. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

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Panchenko, A., Babaev, D., Obiedkov, S. (2015). Large-Scale Parallel Matching of Social Network Profiles. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-26123-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26122-5

  • Online ISBN: 978-3-319-26123-2

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