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Social Influence (Deep) Learning for Human Behavior Prediction

  • Luca Luceri
  • Torsten Braun
  • Silvia Giordano
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Influence propagation in social networks has recently received large interest. In fact, the understanding of how influence propagates among subjects in a social network opens the way to a growing number of applications. Many efforts have been made to quantitatively measure the influence probability between pairs of subjects. Existing approaches have two main drawbacks: (i) they assume that the influence probabilities are independent of each other, and (ii) they do not consider the actions not performed by the subject (but performed by her/his friends) to learn these probabilities. In this paper, we propose to address these limitations by employing a deep learning approach. We introduce a Deep Neural Network (DNN) framework that has the capability for both modeling social influence and for predicting human behavior. To empirically validate the proposed framework, we conduct experiments on a real-life (offline) dataset of an Event-Based Social Network (EBSN). Results indicate that our approach outperforms existing solutions, by efficiently resolving the limitations previously described.

References

  1. 1.
    Newman, M.E.: The structure and function of complex networks. SIAM (2003)Google Scholar
  2. 2.
    Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)ADSMathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)CrossRefGoogle Scholar
  4. 4.
    Singla, P., Richardson, M.: Yes, there is a correlation:-from social networks to personal behavior on the web. In: International Conference on World Wide Web (2008)Google Scholar
  5. 5.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: International Conference on Knowledge Discovery and Data Mining (2006)Google Scholar
  6. 6.
    Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2008)Google Scholar
  7. 7.
    Luceri, L., Vancheri, A., Braun, T., Giordano, S.: On the social influence in human behavior: physical, homophily, and social communities. In: International Conference on Complex Networks and Their Applications, Springer (2017)Google Scholar
  8. 8.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: ACM International Conference on Knowledge Discovery and Data Mining, ACM (2001)Google Scholar
  9. 9.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: ACM International Conference on Knowledge Discovery and Data Mining, ACM (2002)Google Scholar
  10. 10.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, ACM (2003)Google Scholar
  11. 11.
    Kimura, M., Saito, K., Nakano, R.: Extracting influential nodes for information diffusion on a social network. AAAI 7, 1371–1376 (2007)Google Scholar
  12. 12.
    Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: International Conference on World Wide Web, ACM (2004)Google Scholar
  13. 13.
    Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. Knowledge-Based Intelligent Information and Engineering Systems (2008)Google Scholar
  14. 14.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: International Conference on Knowledge Discovery and Data Mining, ACM (2009)Google Scholar
  15. 15.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: ACM International Conference on Web Search and Data Mining, ACM (2010)Google Scholar
  16. 16.
    Liu, L., Tang, J., Han, J., Yang, S.: Learning influence from heterogeneous social networks. Data Min. Knowl. Discov. 25(3), 511–544 (2012)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Fang, X., Hu, P.J.H., Li, Z., Tsai, W.: Predicting adoption probabilities in social networks. Inf. Syst. Res. (2013)Google Scholar
  18. 18.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)ADSCrossRefGoogle Scholar
  19. 19.
    Schmidhuber, J.: Deep learning in neural networks. Neural Netw. (2015)Google Scholar
  20. 20.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: the 26th International Conference on World Wide Web (2017)Google Scholar
  21. 21.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE (2016)Google Scholar
  22. 22.
    Liu, X., He, Q., Tian, Y., Lee, W.C., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: ACM International Conference on Knowledge Discovery and Data Mining, ACM (2012)Google Scholar
  23. 23.
    Georgiev, P., Noulas, A., Mascolo, C.: The call of the crowd: event participation in location-based social services. arXiv:1403.7657 (2014)
  24. 24.
    Chollet, F.: Keras (2015). http://keras.io (2017)

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of Applied Sciences and Arts of Southern Switzerland (SUPSI)MannoSwitzerland
  2. 2.University of BernBernSwitzerland

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