Model of Clustering in interaction diffusion information in social network: an evolutionary game-theoretic perspective

Navidi, Hamidreza; Ghasemi, Mahdi

doi:10.22105/dmor.2018.69746

Document Type : original-application paper

Authors

Department of Applied Mathematics, Shahed University, Tehran, Iran.

https://doi.org/10.22105/dmor.2018.69746

Abstract

In the real world, the diffusion of some contagions at the same time on the social network is very important. Due to a large number of contagions that have been diffused on a social network at the same time, in order to reduce the model parameters and also being scalable, the contagions have been grouped and clustered and also all the clusters have been set equal on some of the researches, While this assumption is not acceptable and far from the facts of the real world. To eliminate and fix this issue in this thesis, we try to elaborate the different categories of contagions and consider, analyze and study the whole story as evolutionary game theory and will calculate its evolution dynamics of contagions and evolutionarily stable strategy. Evolutionary dynamics and evolutionarily stable of the contagions show the impact of a contagion in the process of diffusion from the point of view of users in two consecutive interval timing and whether it is promoted or suppressed.

Keywords

Main Subjects

Game Theory

References

نویدی، ح؛ کتابچی، س؛ مسی بیدگلی، م. (1390). مدخلی بر نظریه بازی‌ها. چاپ اول، دانشگاه شاهد، تهران.

فرابورس ایران.(1397).دریافت از http://www.ifb.ir/Default.aspx

Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, February). Everyone's an influencer: quantifying influence on twitter. Proceedings of the fourth ACM international conference on web search and data mining (pp. 65-74). ACM.

Bharathi, S., Kempe, D., & Salek, M. (2007, December). Competitive influence maximization in social networks. International workshop on web and internet economics (pp. 306-311). Springer, Berlin, Heidelberg.

Budak, C., Agrawal, D., & El Abbadi, A. (2011, March). Limiting the spread of misinformation in social networks. Proceedings of the 20th international conference on world wide web (pp. 665-674). ACM.

Cosley, D., Huttenlocher, D. P., Kleinberg, J. M., Lan, X., & Suri, S. (2010). Sequential Influence Models in Social Networks. ICWSM, 10, 26.

Galuba, W., Aberer, K., Chakraborty, D., Despotovic, Z., & Kellerer, W. (2010). Outtweeting the twitterers-predicting information cascades in microblogs. WOSN, 10, 3-11.

Gomez Rodriguez, M., Leskovec, J., & Krause, A. (2010). Inferring networks of diffusion and influence. Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1019-1028). ACM.

Jafari, S., & Navidi, H. (2018). A game-theoretic approach for modeling competitive diffusion over social networks. Games, 9(1), 8.

Karrer, B., & Newman, M. E. (2011). Competing epidemics on complex networks. Physical review E, 84(3), 036106.

Kuhlman, C. J., Kumar, V. A., Marathe, M. V., Swarup, S., Tuli, G., Ravi, S. S., & Rosenkrantz, D. J. (2011). Inhibiting the diffusion of contagions in Bi-Threshold systems: analytical and experimental results. AAAI fall symposium: complex adaptive systems.Arlington, Virginia, USA.

Narayanam, R., & Narahari, Y. (2011). A shapley value-based approach to discover influential nodes in social networks. IEEE transactions on automation science and engineering, 8(1), 130-147.

Pathak, N., Banerjee, A., & Srivastava, J. (2010, December). A generalized linear threshold model for multiple cascades. 2010 IEEE 10th international conference on Data Mining (ICDM) (pp. 965-970). IEEE.

Prakash, B. A., Beutel, A., Rosenfeld, R., & Faloutsos, C. (2012, April). Winner takes all: competing viruses or ideas on fair-play networks. Proceedings of the 21st international conference on World Wide Web (pp. 1037-1046). ACM.

Sadikov, E., Medina, M., Leskovec, J., & Garcia-Molina, H. (2011). Correcting for missing data in information cascades. Proceedings of the fourth ACM international conference on web search and data mining (pp. 55-64). ACM.

Su, Y., Zhang, X., Liu, L., Song, S., & Fang, B. (2016). Understanding information interactions in diffusion: an evolutionary game-theoretic perspective. Frontiers of computer science, 10(3), 518-531.

Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012). Competition among memes in a world with limited attention. Scientific reports, 2, 335.

Journal of Decisions and Operations Research

Model of Clustering in interaction diffusion information in social network: an evolutionary game-theoretic perspective

References

References

Volume 3, Issue 3
December 2018
Pages 223-235

Model of Clustering in interaction diffusion information in social network: an evolutionary game-theoretic perspective

References

References

Volume 3, Issue 3December 2018Pages 223-235

Volume 3, Issue 3
December 2018
Pages 223-235