Abstract
Different group of information, such as advertising and product promotion, compete with each other as they diffuse over social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself, without considering human characteristics. This paper uses a framework of social evolutionary game to simulate the dissemination and adjusts utility function and updating mechanism based on coordination game. We find that individuals consider more about their own reputation and more communication between them, individuals are more cautious in the face of strategy choice. When the benefit of competitive information is nearly 1.2 times of the original one, it can make up the loss of reputation caused by changing strategy. For the specific network environment based on simulation, the actual data on Sina Weibo strongly verify this rule and shows that factor of reputation promotes the cooperation and users won’t easily change their information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Y., Yu, J., Qu, W., Shen, H., Cheng, X., Lin, C.: Evolutionary game model and analysis methods for network group behavior. Chin. J. Comput. 38(2), 282–300 (2015)
Yu, J., Wang, Y., Jin, X., et al.: Evolutionary analysis on online social networks using a social evolutionary game. In: Proceedings of the 23rd International Conference on World Wide Web Companion, pp. 415–416. ACM (2014)
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)
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83, 1420–1443 (1978)
Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol.115, No.772, pp. 700–721. The Royal Society (1927)
Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 33–41. ACM (2012)
Beutel, A., Prakash, B.A., Rosenfeld, R., et al.: Interacting viruses in networks: can both survive?. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2012)
Ten Kate, S., Haverkamp, S., Mahmood, F., et al.: Social network influences on technology acceptance: a matter of tie strength, centrality and density. In: BLED 2010 Proceedings Paper, p. 40 (2010)
Scott, J.: Social network analysis: developments, advances, and prospects. Soc. Netw. Anal. Min. 1(1), 21–26 (2011)
Pathak, N., Banerjee, A., Srivastava, J.: A generalized linear threshold model for multiple cascades. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 965–970. IEEE (2010)
Prakash, B.A., Beutel, A., Rosenfeld, R., et al.: Winner takes all: competing viruses or ideas on fair-play networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 1037–1046. ACM (2012)
Weng, L., Flammini, A., Vespignani, A., et al.: Competition among memes in a world with limited attention. Nat. Sci. Rep. 2(7391), 1–8 (2012)
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)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM (2002)
Kostka, J., Oswald, Y.A., Wattenhofer, R.: Word of mouth: rumor dissemination in social networks. In: Shvartsman, A.A., Felber, P. (eds.) SIROCCO 2008. LNCS, vol. 5058, pp. 185–196. Springer, Heidelberg (2008)
Zinoviev, D., Duong, V.: A game theoretical approach to broadcast information diffusion in social networks. In: Proceedings of the 44th Annual Simulation Symposium, pp. 47–52. Society for Computer Simulation International (2011)
Zinoviev, D., Duong, V., Zhang, H.: A game theoretical approach to modeling information dissemination in social networks. Comput. Sci. Game Theor. I, 407–412 (2010). arXiv preprint
Yu, J., Wang, Y., Li, J., et al.: Analysis of competitive information dissemination in social network based on evolutionary game model. In: 2012 Second International Conference on Cloud and Green Computing (CGC), pp. 748–753. IEEE (2012)
Yu, J., Wang, Y., Jin, X., Cheng, X.: Social evolutionary games. In: 2014 5th International Conference on GameNets, pp. 1–5. IEEE (2014)
Andrea, M., Amin, S.: The spread of innovations in social networks. J. Proc. Nat. Acad. Sci. 107(47), 20196–20201 (2010)
Fu, F., Hauert, C., Nowak, M.A., et al.: Reputation-based partner choice promotes cooperation in social networks. Phys. Rev. E 78(2), 026117 (2008)
Santos, F.C., Pacheco, J.M., Lenaerts, T.: Cooperation prevails when individuals adjust their social ties. PLoS Comput. Biol. 2(10), 1284–1291 (2006)
Szabo, G., Toke, C.: Evolutionary prisoner’s dilemma game on a square lattice. Phys. Rev. E 58(1), 69 (1998)
Acknowledgments
This work is supported by National Grand Fundamental Research 973 Program of China (No. 2014CB340401), National Natural Science Foundation of China (No. 61572469, 61173008, 61303244, 61402442, 61402022, 61370132, 61303049).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lu, Y., Wang, Y., Yu, J., Li, J., Liu, L. (2016). Mechanism Analysis of Competitive Information Synchronous Dissemination in Social Networks. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-45814-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45813-7
Online ISBN: 978-3-319-45814-4
eBook Packages: Computer ScienceComputer Science (R0)