Advertisement

BASS: A Bootstrapping Approach for Aligning Heterogenous Social Networks

  • Xuezhi CaoEmail author
  • Yong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9851)

Abstract

Most people now participate in more than one online social network (OSN). However, the alignment indicating which accounts belong to same natural person is not revealed. Aligning these isolated networks can provide united environment for users and help to improve online personalization services. In this paper, we propose a bootstrapping approach BASS to recover the alignment. It is an unsupervised general-purposed approach with minimum limitation on target networks and users, and is scalable for real OSNs. Specifically, we jointly model user consistencies of usernames, social ties, and user generated contents, and then employ EM algorithm for the parameter learning. For analysis and evaluation, We collect and publish large-scale data sets covering various types of OSNs and multi-lingual scenarios. We conduct extensive experiments to demonstrate the performance of BASS, concluding that our approach significantly outperform state-of-the-art approaches.

Keywords

Network alignment Heterogenous networks User modeling 

References

  1. 1.
    Abel, F., Henze, N., Herder, E., Krause, D.: Interweaving public user profiles on the web. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 16–27. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13470-8_4 CrossRefGoogle Scholar
  2. 2.
    Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW, pp. 181–190. ACM (2007)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Inf. Sci. 179(1), 16–32 (2009)CrossRefGoogle Scholar
  5. 5.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  6. 6.
    Frankowski, D., Cosley, D., Sen, S., Terveen, L., Riedl, J.: You are what you say: privacy risks of public mentions. In: SIGIR, pp. 565–572. ACM (2006)Google Scholar
  7. 7.
    Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., Teixeira, R.: Exploiting innocuous activity for correlating users across sites. In: WWW, pp. 447–458. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  8. 8.
    Houvardas, J., Stamatatos, E.: N-gram feature selection for authorship identification. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS (LNAI), vol. 4183, pp. 77–86. Springer, Heidelberg (2006). doi: 10.1007/11861461_10 CrossRefGoogle Scholar
  9. 9.
    Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. In: ICWSM (2011)Google Scholar
  10. 10.
    Kong, X., Zhang, J., Yu, P.S.: Inferring anchor links across multiple heterogeneous social networks. In: CIKM. ACM (2013)Google Scholar
  11. 11.
    Krishnamurthy, B., Wills, C.E.: On the leakage of personally identifiable information via online social networks. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 7–12. ACM (2009)Google Scholar
  12. 12.
    Kumar, S., Zafarani, R., Liu, H.: Understanding user migration patterns in social media. In: AAAI (2011)Google Scholar
  13. 13.
    Labitzke, S., Taranu, I., Hartenstein, H.: What your friends tell others about you: low cost linkability of social network profiles. In: Proceeding of 5th International ACM Workshop on Social Network Mining and Analysis, San Diego (2011)Google Scholar
  14. 14.
    Liu, J., Zhang, F., Song, X., Song, Y.I., Lin, C.Y., Hon, H.W.: What’s in a name?: an unsupervised approach to link users across communities. In: WSDM, pp. 495–504. ACM (2013)Google Scholar
  15. 15.
    Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: Hydra: Large-scale social identity linkage via heterogeneous behavior modeling. In: SIGMOD (2014)Google Scholar
  16. 16.
    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
  17. 17.
    Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)Google Scholar
  19. 19.
    Nunes, A., Calado, P., Martins, B.: Resolving user identities over social networks through supervised learning and rich similarity features. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 728–729. ACM (2012)Google Scholar
  20. 20.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Tan, S., Guan, Z., Cai, D., Qin, X., Bu, J., Chen, C.: Mapping users across networks by manifold alignment on hypergraph. In: AAAI (2014)Google Scholar
  22. 22.
    Vosecky, J., Hong, D., Shen, V.Y.: User identification across multiple social networks. In: First International Conference on Networked Digital Technologies, NDT 2009, pp. 360–365. IEEE (2009)Google Scholar
  23. 23.
    Zafarani, R., Liu, H.: Connecting corresponding identities across communities. In: ICWSM (2009)Google Scholar
  24. 24.
    Zafarani, R., Liu, H.: Connecting users across social media sites: a behavioral-modeling approach. In: SIGKDD, pp. 41–49. ACM (2013)Google Scholar
  25. 25.
    Zhang, J., Kong, X., Yu, P.S.: Transferring heterogeneous links across location-based social networks. In: WSDM, pp. 303–312. ACM (2014)Google Scholar
  26. 26.
    Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. JASIST 57(3), 378–393 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Apex Data and Knowledge Management LabShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations