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An evolutionary game competitive model for increasing accuracy and maximizing influence in social networks | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 156، دوره 13، شماره 1، خرداد 2022، صفحه 1897-1907 اصل مقاله (348.21 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.22669.2398 | ||
| نویسندگان | ||
| Saeedeh Noori1؛ Hamidreza Navidi* 1؛ Reza Bakhtiari2 | ||
| 1Department of Mathematics and Computer Science, Shahed University, Tehran, Iran | ||
| 2Department of Computer Science, Shahed University, Tehran, Iran | ||
| تاریخ دریافت: 26 بهمن 1399، تاریخ پذیرش: 17 شهریور 1400 | ||
| چکیده | ||
| Given the importance of maximizing influence in a social network, studies in this field often seek to find the nodes that have the most influence on the social network if designated as primary seeds. In this study, to reduce the complexity of computation algorithms, the problem is divided into several groups that aim to find a group of influential people among users of a social network. In this paper, a framework is introduced for solving the problem of influence maximization, which is based on the member clustering by the K means method, to improve the classification of network users, the data are weighted and the problem is modeled and analyzed as an evolutionary game. Finally, calculate its evolutionary stable strategy. This framework has been tested on real social network data for Abrar University students and we have achieved results such as increasing classification accuracy, reducing error function and finding a stable strategy in the community. | ||
| کلیدواژهها | ||
| Social network؛ Influence maximization؛ Distinction factor؛ K-means clustering؛ Evolutionary stable strategy | ||
| مراجع | ||
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[1] H. Abdoli, Game theory and its applications (incomplete, evolutionary and collaborative information games), Organization for the Study and Compilation of University Humanities Books (Samat), Center for Research and Development of Humanities, 2013. [2] P. Domingos and M. Richardson, Mining the network value of customers, In: Proc. Seventh ACM SIGKDD Int. Conf. Knowledge Discovery and Data Min., ACM, 2001. [3] J. Kaksury, Maximize influence in social networks using genetic algorithms, Iran Doc, 2017. [4] D. Kempe, J. M. Kleinberg and É. Tardos, Maximizing the spread of influence through a social network, In Proc. 9th ACM SIGKDD Conf. Knowledge Discovery and Data Min., 2003, pp. 137–146. [5] M. A. A. Kermani, A. Aliahmadi and R. Hanneman, Optimizing the choice of influential nodes for diffusion on a social network, Int. J. Commun. Syst., 29(7) 2016 1235-1250. [6] S. Jafari and H. Navidi, A game-theoretic approach for modeling competitive diffusion over social networks, Games, 9(1)(2018) 8. [7] E. Lotfi and H. Navidi. A new model for determining the level of OPEC oil production based on price forecasting and game theory, J. Model. Eng., 11 (35). [8] M. H. Manshei and P. Mousavi, Sustainable evolutionary strategies in maximizing the influence of social networks, Ministry of Science, Research and Technology - Isfahan University of Technology - Faculty of Electrical and Computer Science, 2014. [9] M. Momeni, Data clustering (cluster analysis), Mansour Momeni, 2011. [10] H. Navidi, S. Ketabchi and M. Masibidgoli, An Introduction to Game Theory, Shahed University, Vice Chancellor for Research, Printing and Publishing Center, 2011. [11] M. Richardson and P. Domingos, Mining knowledge-sharing sites for viral marketing, In: Proc. Eighth ACM SIGKDD Int. Conf. Knowledge Discovery and Data Min., ACM, 2002. [12] M. Rostamnia, Maximizing the influence of social networks with the help of cascading model, 2017. [13] J. M. Smith, The theory of games and the evolution of animal conflicts, J. Theor. Biol. , 47 (1) (1974) 209-221. | ||
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