Abstract
How to model the influence propagation accurately in social network is a critical and challenge task. Although numerous attempts have been made for this topic, few of them consider the user’s negative influence. Positive influence will encourage people to perform some action while the negative one will degrade the probability. Thus, it is meaningful to model the influence propagation by considering both the positive and negative influence. What’s more, previous research is mostly based on the assumption that the influence probabilities between users are known, however, they are typically unknown in real-world social networks. To address these problems, a novel Multipolar Factors aware Independent Cascade model (MFIC) is proposed to outline the information diffusion in social network. Then, the user-to-user influence probability is learnt with the users’ behavior logs based on the EM algorithm. We also apply the discovered influence probabilities to user behavior prediction. Experiments are conducted over real data sets, Flixster and Digg, validating the effectiveness of our methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD, pp. 57–66, August 2003
Huang, J., Cheng, X.Q., Shen, H.W., et al.: Exploring social influence via posterior effect of word-of-mouth recommendation. In: WSDM, pp. 573–582, February 2012
Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: SIGIR, pp. 671–680, August 2012
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: KDD, pp. 807–816, June 2009
Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. In: ICDM, pp. 81–90 (2012)
Li, D., Xu, Z., Luo, Y., Li, S.: Modeling information diffusion over social networks for temporal dynamic prediction. In: CIKM, pp. 1477–1480, October 2013
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038, July 2010
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: KDD, pp. 199–208, June 2009
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)
Goyal, A., Bonchi, F., Lakshmanan, L.: Learning influence probabilities in social networks. In: WSDM, pp. 241–250, February 2010
Li, H., Bhowmick, S.S., Casino, S.A.: Towards conformity-aware social influence analysis in online social networks. In: CIKM, pp. 1007–1012, August 2011
Chen, W., Collins, A., Cummings, R., et al.: Influence maximization in social networks when negative opinions may emerge and propagate. In: SDM, pp. 379–390, April 2011
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, W., Peng, Z., Liu, Z., Zhu, T., Hong, X. (2015). Learning the Influence Probabilities Based on Multipolar Factors in Social Network. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-25159-2_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25158-5
Online ISBN: 978-3-319-25159-2
eBook Packages: Computer ScienceComputer Science (R0)