Advertisement

Information Network Cascading and Network Re-construction with Bounded Rational User Behaviors

  • Guanxiang YunEmail author
  • Qipeng P. Zheng
  • Vladimir Boginski
  • Eduardo L. Pasiliao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)

Abstract

Social media platforms have become increasingly used for both socialization and information diffusion. For example, commercial users can improve their profits by expanding their social media connections to new users. In order to optimize an information provider’s network connections, this paper establishes a mathematical model to simulate the behaviours of other users to build connections within the information provider’s network. The behaviours include information reposting and following/unfollowing other users. We apply the linear threshold propagation model to determine the reposting actions. In addition, the following or unfollowing actions are modeled by the boundedly rational user equilibrium (BRUE). A three-level optimization model is proposed to maximize total number of connections, which is the goal of the top level. The second level is to simulate user behaviours under BRUE. The third or bottom level is to maximize the other users’ utility used in the second level. This paper solves this problem by using exact algorithms for a small-scale synthetic network.

Keywords

Boundedly rational user equilibrium Information network Large neighborhood search Linear threshold 

Notes

Acknowledgement

We would like to thank Luke Fuller for assistance with detailed proof reading and comments that greatly improved the manuscript.

References

  1. 1.
    Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 539–550. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-17572-5_48CrossRefGoogle Scholar
  2. 2.
    Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: 2010 IEEE International Conference on Data Mining, pp. 88–97. IEEE (2010)Google Scholar
  3. 3.
    Di, X., Liu, H.X., Pang, J.S., Ban, X.J.: Boundedly rational user equilibria (BRUE): mathematical formulation and solution sets. Transp. Res. Part B Methodol. 57, 300–313 (2013)CrossRefGoogle Scholar
  4. 4.
    Elsadany, A.: Competition analysis of a triopoly game with bounded rationality. Chaos, Solitons Fractals 45(11), 1343–1348 (2012)CrossRefGoogle Scholar
  5. 5.
    Goyal, A., Lu, W., Lakshmanan, L.V.: Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 211–220. IEEE (2011)Google Scholar
  6. 6.
    Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  7. 7.
    Han, K., Szeto, W., Friesz, T.L.: Formulation, existence, and computation of boundedly rational dynamic user equilibrium with fixed or endogenous user tolerance. Transp. Res. Part B Methodol. 79, 16–49 (2015)CrossRefGoogle Scholar
  8. 8.
    Kahneman, D.: Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93(5), 1449–1475 (2003)CrossRefGoogle Scholar
  9. 9.
    Kasthurirathna, D., Piraveenan, M.: Emergence of scale-free characteristics in socio-ecological systems with bounded rationality. Sci. Rep. 5, 10448 (2015)CrossRefGoogle Scholar
  10. 10.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
  11. 11.
    Laporte, G., Musmanno, R., Vocaturo, F.: An adaptive large neighbourhood search heuristic for the capacitated arc-routing problem with stochastic demands. Transp. Sci. 44(1), 125–135 (2010)CrossRefGoogle Scholar
  12. 12.
    Lou, Y., Yin, Y., Lawphongpanich, S.: Robust congestion pricing under boundedly rational user equilibrium. Transp. Res. Part B Methodol. 44(1), 15–28 (2010)CrossRefGoogle Scholar
  13. 13.
    Lovejoy, K., Saxton, G.D.: Information, community, and action: How nonprofit organizations use social media. J. Comput. Mediated Commun. 17(3), 337–353 (2012)CrossRefGoogle Scholar
  14. 14.
    Mahmassani, H.S., Chang, G.L.: On boundedly rational user equilibrium in transportation systems. Transp. Sci. 21(2), 89–99 (1987)CrossRefGoogle Scholar
  15. 15.
    Paniagua, J., Sapena, J.: Business performance and social media: Love or hate? Bus. Horiz. 57(6), 719–728 (2014)CrossRefGoogle Scholar
  16. 16.
    Roberts, K.H., Stout, S.K., Halpern, J.J.: Decision dynamics in two high reliability military organizations. Manage. Sci. 40(5), 614–624 (1994)CrossRefGoogle Scholar
  17. 17.
    Rötheli, T.F.: Boundedly rational banks contribution to the credit cycle. J. Soc. Econ. 41(5), 730–737 (2012)CrossRefGoogle Scholar
  18. 18.
    Simon, H.A.: Models of man; social and rational (1957)Google Scholar
  19. 19.
    Simon, H.A.: Theories of bounded rationality. Decis. Organ. 1(1), 161–176 (1972)MathSciNetGoogle Scholar
  20. 20.
    Simon, H.A.: Bounded rationality and organizational learning. Organ. Sci. 2(1), 125–134 (1991)CrossRefGoogle Scholar
  21. 21.
    Simon, H.A.: Models of Bounded Rationality: Empirically Grounded Economic Reason, vol. 3. MIT Press, Cambridge (1982)Google Scholar
  22. 22.
    Singh, V.K., Jain, R., Kankanhalli, M.S.: Motivating contributors in social media networks. In: Proceedings of the First SIGMM Workshop on Social Media, pp. 11–18. ACM (2009)Google Scholar
  23. 23.
    Su, Z., Xu, Q., Fei, M., Dong, M.: Game theoretic resource allocation in media cloud with mobile social users. IEEE Trans. Multimedia 18(8), 1650–1660 (2016)CrossRefGoogle Scholar
  24. 24.
    Van Dijck, J.: The Culture of Connectivity: A Critical History of Social Media. Oxford University Press, Oxford (2013)CrossRefGoogle Scholar
  25. 25.
    Van Dijck, J.: Facebook and the engineering of connectivity: a multi-layered approach to social media platforms. Convergence 19(2), 141–155 (2013)CrossRefGoogle Scholar
  26. 26.
    Wüstenhagen, R., Menichetti, E.: Strategic choices for renewable energy investment: conceptual framework and opportunities for further research. Energy Policy 40, 1–10 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guanxiang Yun
    • 1
    Email author
  • Qipeng P. Zheng
    • 1
  • Vladimir Boginski
    • 1
  • Eduardo L. Pasiliao
    • 2
  1. 1.University of Central FloridaOrlandoUSA
  2. 2.Air Force Research LaboratoryEglin AFBUSA

Personalised recommendations