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Large-Scale Parallel Matching of Social Network Profiles

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (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.

Keywords

User identify resolution Entity resolution Profile matching Record linkage Social networks Social network analysis Facebook Vkontakte 
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Notes

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.

References

  1. 1.
    Bartunov, S., Korshunov, A., Park, S.T., Ryu, W., Lee, H.: Joint link-attribute user identity resolution in online social networks. In: Proceedings of the Sixth SNA-KDD Workshop at KDD (2012)Google Scholar
  2. 2.
    Balduzzi, M., Platzer, C., Holz, T., Kirda, E., Balzarotti, D., Kruegel, C.: Abusing social networks for automated user profiling. In: Jha, S., Sommer, R., Kreibich, C. (eds.) RAID 2010. LNCS, vol. 6307, pp. 422–441. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  3. 3.
    Wondracek, G., Holz, T., Kirda, E., Kruegel, C.: A practical attack to de-anonymize social network users. In: 2010 IEEE Symposium on Security and Privacy (SP), pp. 223–238. IEEE (2010)Google Scholar
  4. 4.
    Goga, O., Perito, D., Lei, H., Teixeira, R., Sommer, R.: Large-scale correlation of accounts across social networks. Technical report, International Computer Science Institute (2013)Google Scholar
  5. 5.
    Sironi, G.: Automatic alignment of user identities in heterogeneous social networks. Master’s thesis, Politechnico di Milano, Italy (2012)Google Scholar
  6. 6.
    Veldman, I.: Matching profiles from social network sites: Similarity calculations with social network support. Master’s thesis, University of Twente, Italy (2009)Google Scholar
  7. 7.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)Google Scholar
  8. 8.
    Raad, E., Chbeir, R., Dipanda, A.: User profile matching in social networks. In: 13th International Conference on Network-Based Information Systems (NBiS), pp. 297–304. IEEE (2010)Google Scholar
  9. 9.
    Malhotra, A., Totti, L., Meira Jr., W., Kumaraguru, P., Almeida, V.: Studying user footprints in different online social networks. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 1065–1070. IEEE Computer Society (2012)Google Scholar
  10. 10.
    Jain, P., Kumaraguru, P., Joshi, A.: @I seek ‘fb.me’: identifying users across multiple online social networks. In: Proceedings of the 22nd International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp. 1259–1268 (2013)Google Scholar
  11. 11.
    Boytsov, L.: Indexing methods for approximate dictionary searching: comparative analysis. J. Exp. Algorithmics (JEA) 16, 1–1 (2011)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Du, M.: Approximate name matching. NADA, Numerisk Analys och Datalogi, KTH, Kungliga Tekniska Högskolan. Stockholm: un (2005)Google Scholar
  13. 13.
    Navarro, G., Baeza-Yates, R., Marcelo Azevedo Arcoverde, J.: Matchsimile: a flexible approximate matching tool for searching proper names. J. Am. Soc. Inf. Sci. Technol. 54(1), 3–15 (2003)CrossRefGoogle Scholar
  14. 14.
    Lisbach, B., Meyer, V.: Linguistic Identity Matching. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  15. 15.
    Schulz, K., Mihov, S.: Fast string correction with Levenshtein-automata. Int. J. Doc. Anal. Recogn. 5, 67–85 (2002)CrossRefzbMATHGoogle Scholar
  16. 16.
    Petrovsky, N.: Dictionary of Russian personal names. http://www.gramota.ru/slovari/info/petr M.: In Russian Dictionaries (2000)
  17. 17.
    Trotman, A.: Learning to rank. Inf. Retrieval 8(3), 359–381 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.FG Language TechnologyTU DarmstadtDarmstadtGermany
  2. 2.Tinkoff Credit Systems Inc.MoscowRussia
  3. 3.National Research University Higher School of EconomicsMoscowRussia

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