Parallel Data-Driven Modeling of Information Spread in Social Networks

  • Oksana Severiukhina
  • Klavdiya Bochenina
  • Sergey Kesarev
  • Alexander Boukhanovsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10860)


Models of information spread in social networks are widely used to explore the drivers of content contagion and to predict the effect of new information messages. Most of the existing models (aggregated as SIR-like or network-based as independent cascades) use the assumption of homogeneity of an audience. However, to make a model plausible for a description of real-world processes and to measure the accumulated impact of information on individuals, one needs to personalize the characteristics of users as well as sources of information. In this paper, we propose an approach to data-driven simulation of information spread in social networks which combines a set of different models in a unified framework. It includes a model of a user (including sub-models of reaction and daily activity), a model of message generation by information source and a model of message transfer within a user network. The parameters of models (e.g. for different types of agents) are identified by data from the largest Russian social network For this study, we collected the network of users associated with charity community (~33.7 million nodes). To tackle with huge size of networks, we implemented parallel version of modeling framework and tested it on the Lomonosov supercomputer. We identify key parameters of models that may be tuned to reproduce observable behavior and show that our approach allows to simulate aggregated dynamics of reactions to a series of posts as a combination of individual responses.


Multi-agent modeling Information spreading Parallel computing Social networks Complex networks Data-driven model 


  1. 1.
  2. 2.
    Li, M., Wang, X., Gao, K., Zhang, S.: A survey on information diffusion in online social networks: models and methods. Information 8, 118 (2017). Scholar
  3. 3.
    Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence: models, analysis and simulation. J. Artif. Soc. Soc. Simul. (JASSS) 5(3), (2002)Google Scholar
  4. 4.
    Leifeld, P.: Polarization of coalitions in an agent-based model of political discourse. Leifeld Comput. Soc. Netw. 1, 1–22 (2014)CrossRefGoogle Scholar
  5. 5.
    Lambiotte, R., Ausloos, M., Hołyst, J.A.: Majority model on a network with communities. Phys. Rev. E 75, 30101 (2007). Scholar
  6. 6.
    Yu, Y., Xiao, G., Li, G., Tay, W.P., Teoh, H.F.: Opinion diversity and community formation in adaptive networks. Chaos: Interdiscip. J. Nonlinear Sci. 27, 103115 (2017). Scholar
  7. 7.
    Lu, X., Yu, Z., Guo, B., Zhou, X.: Modeling and predicting the re-post behavior in Sina Weibo. In: Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013, pp. 962–969 (2013)Google Scholar
  8. 8.
    Liu, L., Qu, B., Chen, B., Hanjalic, A., Wang, H.: Modeling of information diffusion on social networks with applications to WeChat, 1–17 (2017).
  9. 9.
    Lande, D.V, Hraivoronska, A.M., Berezin, B.O.: Agent-based model of information spread in social networks, 7 p. (2016)Google Scholar
  10. 10.
    Ryczko, K., Domurad, A., Buhagiar, N., Tamblyn, I.: Hashkat: large-scale simulations of online social networks. Soc. Netw. Anal. Min. 7, 4 (2017). Scholar
  11. 11.
    Mei, S., Zarrabi, N., Lees, M., Sloot, P.M.A.: Complex agent networks: an emerging approach for modeling complex systems. Appl. Soft Comput. J. 37, 311–321 (2015). Scholar
  12. 12.
    Gatti, M., Cavalin, P., Neto, S.B., Pinhanez, C., dos Santos, C., Gribel, D., Appel, A.P.: Large-scale multi-agent-based modeling and simulation of microblogging-based online social network. In: Alam, S.J., Van Dyke Parunak, H. (eds.) MABS 2013. LNCS (LNAI), vol. 8235, pp. 17–33. Springer, Heidelberg (2014). Scholar
  13. 13.
    Vega-Oliveros, D.A., Berton, L., Vazquez, F., Rodrigues, F.A.: The impact of social curiosity on information spreading on networks (2017).
  14. 14.
    Sayin, B., Şahin, S.: A novel approach to information spreading models for social networks. In: Sixth International Conference on Data Analytics III, DATA Analytics 2017 (2017)Google Scholar
  15. 15.
    Zhu, Z.Q., Liu, C.J., Wu, J.L., Xu, J., Liu, B.: The influence of human heterogeneity to information spreading. J. Stat. Phys. 154, 1569–1577 (2014). Scholar
  16. 16.
    Ou, C., Jin, X., Wang, Y., Cheng, X.: Modelling heterogeneous information spreading abilities of social network ties. Simul. Model. Pract. Theory 75, 67–76 (2017). Scholar
  17. 17.
    Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R.: The independent cascade and linear threshold models. Diffusion in Social Networks. SCS, pp. 35–48. Springer, Cham (2015). Scholar
  18. 18.
    van Maanen, P.P., van der Vecht, B.: An agent-based approach to modeling online social influence. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2013).
  19. 19.
    Raghavan, V., Ver Steeg, G., Galstyan, A., Tartakovsky, A.G.: Coupled hidden markov models for user activity in social networks. In: 2013 IEEE International Conference on Multimedia Expo Work (ICMEW), pp. 1–6 (2013).
  20. 20.
    Bochenina, K., Kesarev, S., Boukhanovsky, A.: Scalable parallel simulation of dynamical processes on large stochastic Kronecker graphs. Future Gener. Comput. Syst. 78, 502–515 (2017). Scholar
  21. 21.
    Sadovnichy, V., Tikhonravov, A., Voevodin, V., Opanasenko, V.: “Lomonosov”: supercomputing at Moscow State University. In: Contemporary High Performance Computing: From Petascale Toward Exascale (Chapman & Hall/CRC Computational Science). CRC Press, Boca Raton, pp. 283–307 (2013)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Oksana Severiukhina
    • 1
  • Klavdiya Bochenina
    • 1
  • Sergey Kesarev
    • 1
  • Alexander Boukhanovsky
    • 1
  1. 1.ITMO UniversitySaint PetersburgRussia

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