Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Li Liu This email address is being protected from spambots. You need JavaScript enabled to view it.1, Yuan Lu1 , Yuan-Zhuo Wang2 and Jian-Ye Yu3

1School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China
2Institute of Computing Technology, Chinese Academy of Science, 100080, P.R. China
3School of Information, Beijing Wuzi University, Beijing 101149, P.R. China


 

Received: March 2, 2016
Accepted: September 5, 2016
Publication Date: March 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.1.15  

ABSTRACT


Information competes with each other as it disseminates over the social networks, and the factors that influence the synchronous spread have been attracting the academic interest. In this paper, a framework of social evolutionary games is used to investigate the evolution process of the information spreading through social networks, which a coevolutionary mechanism is adopted by individuals who aim to improve not only their short-term utility, but also their own long-term reputation affected by the information accepted. Meanwhile the strategy of coordination game is used to describe the behavior of competition in the information diffusion. Several simulations are performed by the proposed model to analyze the factors that influence the synchronous spread of the competitive information. Simulation results indicate that the individual’s reputation plays a certain role in the dissemination of information. And thereafter,we observe how the competitive information dissemination predicted by our model works in a real scenario.


Keywords: Social Network, Information Dissemination, Evolutionary Game, Coordination Game


REFERENCES


  1. [1] Fang, B. X., Online Social Network Analysis, Beijing: Publishing House of Electronics Industry (2014).
  2. [2] Myers, S. A. and Leskovec, J., “CLASH of the contagions: Cooperation and Competition in Information Diffusion. Data Mining (ICDM),” 2012 IEEE 12th International Conference on IEEE, pp. 539548 (2012). doi: 10.1109/ICDM.2012.159
  3. [3] Wang, Y. Z., Yu, J. Y., Qu, W., Shen, H. W., Cheng, X. Q. and Lin, C., “Evolutionary Game Model and Analysis Methods for Network Group Behavior,” Chinese Journal of Computers, Vol. 38, No. 2, pp. 282300 (2012).
  4. [4] Ten Kate, S., Haverkamp, S., Mahmood, F., et al., “Social Network Influences on Technology Acceptance: A Matter of Tie Strength, Centrality and Density,” BLED 2010 Proceedings, Paper, p. 40 (2010).
  5. [5] Scott, J., “Social Network Analysis: Developments, Advances, and Prospects,” Social Network Analysis and Mining, Vol. 1, No. 1, pp. 2126 (2011). doi: 10.1007/s13278-010-0012-6
  6. [6] Pathak, N., Banerjee, A. and Srivastava, J., “A Generalized Linear Threshold Model for Multiple Cascades,” Data Mining (ICDM), 2010 IEEE 10th International Conference on IEEE, pp. 965970 (2010). doi: 10. 1109/ICDM.2010.153
  7. [7] Prakash, B. A., Beutel, A., Rosenfeld, R., et al., “Winner Takes All: Competing Viruses or Ideas on Fairplay Networks,” Proceedings of the 21st International Conference on WORLD Wide Web, ACM, pp. 1037 1046 (2012). doi: 10.1145/2187836.2187975
  8. [8] Weng, L., Flammini, A., Vespignani, A., et al., “Competition among Memes in a World with Limited Attention,” Nature: Scientific Reports, Vol. 2, No. 7391, pp. 18 (2012). doi: 10.1038/srep02304
  9. [9] Iribarren, J. L. and Moro, E., “Impact of Human Activity Patterns on the Dynamics of Information Diffusion,” Physical Review Letters, Vol. 103, No. 3 (2009). doi: 10.1103/PhysRevLett.103.038702
  10. [10] Jiang, Y. and Jiang, J., “Diffusion in Social Networks: A Multiagent Perspective,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, No. 2, pp. 198213 (2015). doi: 10.1109/TSMC.2014.2339198
  11. [11] Jiang, Y. and Jiang, J., “Understanding Social Networks from a Multiagent Perspective,” IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 10, pp. 27432759 (2014). doi: 10.1109/TPDS.2013. 254
  12. [12] Gao, C., Liu, J. and Zhong, N., “Network Immunization with Distributed Autonomy-oriented Entities,” IEEE Transactions on Parallel and Distributed Systems, Vol. 22, No. 7, pp. 12221229 (2011). doi: 10.1109/TPDS.2010.197
  13. [13] Kempe, D., Kleinberg, J. and Tardos, É., “Maximizing the Spread of Influence through a Social Network,” Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 137146 (2003). doi: 10.1145/956755. 956769
  14. [14] Domingos, P. and Richardson, M., “Mining the Network Value of Customers,” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 5766 (2001). doi: 10.1145/502512.502525
  15. [15] Richardson, M. and Domingos, P., “Mining Knowledge-sharing Sites for Viral Marketing,” Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 6170 (2002). doi: 10.1145/775056.775057
  16. [16] Kostka, J., Oswald, Y. A. and Wattenhofer, R., “Word of Mouth: Rumor Dissemination in Social Networks,” Proc. 15th Intl. Colloquium on Structural Information and Communication Complexity, Springer Berlin Heidelberg, pp. 185196 (2008). doi: 10.1007/978-3- 540-69355-0_16
  17. [17] Zinoviev, D. and Duong, V., “A Game Theoretical Approach to Broadcast Information Diffusion in Social Networks,” Proceedings of the 44th Annual Simulation Symposium, Society for Computer Simulation International, pp. 4752 (2011).
  18. [18] Zinoviev, D., Duong, V. and Zhang, H., “A Game Theoretical Approach to Modeling Information Dissemination in Social Networks,” arXiv Preprint Computer Science and Game Theory, Vol. I, pp. 407412 (2010).
  19. [19] Yu, J., Wang, Y., Li, J., et al., “Analysis of Competitive Information Dissemination in Social Network Based on Evolutionary Game Model,” Cloud and Green Computing (CGC), 2012 Second International Conference on IEEE, pp. 748753 (2012). doi: 10.1109/ CGC.2012.126
  20. [20] Yu, J., Wang, Y., Jin, X., et al., “Social Evolutionary Games,” Game Theory for Networks (GAMENETS), 2014 5th International Conference on IEEE, pp. 15 (2014). doi: 10.1109/GAMENETS.2014.7043726
  21. [21] Saberi, A. and Montanari, A., “The Spread of Innovations in Social Networks,” Proceedings of the National Academy of Sciences, Vol. 107, No. 47, pp. 20196 20201 (2010). doi: 10.1073/pnas.1004098107
  22. [22] Tomassini, M. and Pestelacci, E., “Coordination Games on Dynamical Networks,” Games, Vol. 1 No. 3, pp. 242261 (2010). doi: 10.3390/g1030242
  23. [23] Fu, F., Hauert, C., Nowak, M. A., et al., “Reputationbased Partner Choice Promotes Cooperation in Social Networks,” Physical Review E, Vol. 78, No. 2, pp. 026117 (2008). doi: 10.1103/PhysRevE.78.026117
  24. [24] Santos, F. C., Pacheco, J. M. and Lenaerts, T., “Cooperation Prevails When Individuals Adjust Their Social Ties,” PLoS Comput Biol, Vol. 2, No. 10, pp. 12841291 (2006). doi: 10.1371/journal.pcbi.0020140
  25. [25] Szabó, G. and Tke, C., “Evolutionary Prisoner’s Dilemma Game on a Square Lattice,” Physical Review E, Vol. 58, No. 1, pp. 6973 (1998). doi: 10.1103/Phys RevE.58.69


    



 

2.1
2023CiteScore
 
 
69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.