Feature-Enhanced Probabilistic Models for Diffusion Network Inference

  • Liaoruo Wang
  • Stefano Ermon
  • John E. Hopcroft
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7524)


Cascading processes, such as disease contagion, viral marketing, and information diffusion, are a pervasive phenomenon in many types of networks. The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention. However, a major challenge is that the underlying network is often unknown. In this paper, we revisit the problem of inferring latent network structure given observations from a diffusion process, such as the spread of trending topics in social media. We define a family of novel probabilistic models that can explain recurrent cascading behavior, and take into account not only the time differences between events but also a richer set of additional features. We show that MAP inference is tractable and can therefore scale to very large real-world networks. Further, we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10-month period. Experimental results show that our models accurately recover the links of the Twitter network, and significantly improve the performance over previous models based entirely on time.


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  1. 1.
    Gomez-Rodriguez, M., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. In: ICML, pp. 561–568 (2011)Google Scholar
  2. 2.
    Gomez-Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: KDD, pp. 1019–1028 (2010)Google Scholar
  3. 3.
    Myers, S.A., Leskovec, J.: On the convexity of latent social network inference. In: NIPS (2010)Google Scholar
  4. 4.
    Adar, E., Adamic, L.A.: Tracking information epidemics in blogspace. In: Web Intelligence, pp. 207–214 (2005)Google Scholar
  5. 5.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)Google Scholar
  6. 6.
    Lappas, T., Terzi, E., Gunopulos, D., Mannila, H.: Finding effectors in social networks. In: KDD (2010)Google Scholar
  7. 7.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)Google Scholar
  8. 8.
    Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. Journal of Consumer Research 34(4) (2007)Google Scholar
  9. 9.
    Wallinga, J., Teunis, P.: Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology 160(6) (2004)Google Scholar
  10. 10.
    Sheldon, D., Dilkina, B., Elmachtoub, A., Finseth, R., Sabharwal, A., Conrad, J., Gomes, C., Shmoys, D., Allen, W., Amundsen, O., et al.: Maximizing the spread of cascades using network design. In: UAI (2010)Google Scholar
  11. 11.
    Lawless, J.F.: Statistical models and methods for lifetime data (1982)Google Scholar
  12. 12.
    Dahl, J., Vandenberghe, L.: CVXOPT: A Python package for convex optimization. In: Proc. Eur. Conf. Op. Res. (2006)Google Scholar
  13. 13.
    Zhu, C., Byrd, R.H., Lu, P., Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw. 23(4), 550–560 (1997)MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Cavnar, W.B., Trenkle, J.M.: N-gram-based text categorization. In: Proc. 3rd Annual Symposium on Document Analysis and Information Retrieval (1994)Google Scholar
  15. 15.
    Jones, E., Oliphant, T., Peterson, P.: Scipy: Open source scientific tools for Python (2001), http://www.scipy.org/
  16. 16.
    Menon, A.K., Elkan, C.: Link Prediction via Matrix Factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Liaoruo Wang
    • 1
  • Stefano Ermon
    • 1
  • John E. Hopcroft
    • 1
  1. 1.Department of Computer ScienceCornell UniversityIthacaUSA

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