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Литература
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1. |
Easley D., Kleinberg J., Networks, crowds, and markets: Reasoning about a highly connected world, Cambridge University Press, Cambridge, 2010, 819 pp. |
2. |
Karsai M., Iniguez G., Kaski K., Kertesz J., “Complex contagion process in spreading of onlne innovation”, J. R. Soc. Interface, 11 (2014), 20140694, 8 pp. |
3. |
Gleeson J. P., Cahalane D. J., “Seed size strongly affects cascades on random networks”, Phys. Rev. E, 75 (2007), 056103, 4 pp. |
4. |
Barrat A., Barthelemy M., Vespignani A., Dynamical processes on complex networks, Cambridge University Press, Cambridge, 2012, 347 pp. |
5. |
Kocsis G., Kun F., “Competition of information channels in the spreading of innovations”, Phys. Rev. E, 84 (2011), 026111, 7 pp. |
6. |
Airoldi E. M., Blei D. M., Fienberg S. E., Xing E. P., “Mixed membership stochastic blockmodels”, J. Mach. Learn. Res., 9 (2008), 1981–2014 |
7. |
Khvatova T., Block M., Zhukov D., Lesko S., “How to measure trust: The percolation model applied to intra-organisational knowledge sharing networks”, J. Knowl. Manag., 20:5 (2016), 918–935 |
8. |
Khvatova T. Yu., Zaltsman A. D., Zhukov D. O., “Information processes in social networks: Percolation and stochastic dynamics”, CEUR Workshop Proc., 2064, 2017, 277–288 |
9. |
Plikynas D., Raudys A., Raudys S., “Agent-based modelling of excitation propagation in social media groups”, J. Experimental Theoretical Artificial Intelligence, 27:4 (2015), 373–388 |
10. |
Wang A., Wu W., Chen J., “Social network rumors spread model based on cellular automata”, 10th Conference (International) on Mobile Ad-hoc and Sensor Networks Proceedings, IEEE, Piscataway, NJ, USA, 2014, 236–242 |
11. |
Андрианова Е. Г., Головин С. А., Зыков С. В., Лесько С. А., Чукалина Е. Р., “Обзор современных моделей и методов анализа временных рядов динамики процессов в социальных, экономических и социотехнических системах”, Российский технологический ж., 8:4(36) (2020), 7–45 [Andrianova E. G., S. A. Golovin, S. V. Zykov, S. A. Lesko, E. R. Chukalina, “Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems”, Russ. Technological J., 8:4 (2020), 7–45 ] |
12. |
Zhukov D. O., Lesko S. A., Khvatova T. Yu., “Percolation models of information distribution and blocking in social networks”, 5th Ashridge Research Conference (International) Global Disruption and Organisational Innovation, Berkhamsted, U.K., 2016, 23423 |
13. |
Zhukov D., Khvatova T., Zaltsman A., “Stochastic dynamics of influence expansion in social networks and managing users' transitions from one state to another”, 11th European Conference on Information Systems Management Proceedings, Academic Conferences and Publishing International Ltd., Reading, 2017, 322–329 |
14. |
Zhukov D. O., Alyoshkin A. S., Obukhova A. G., “Modelling to be based on systems of differential kinetic equations to processes group selection voters during the electoral campaign of Trump–Clinton 2015–2016”, 7th Conference (International) on Information Communication and Management Proceedings, ACM, New York, NY, USA, 2017, 88–94 |
15. |
Sigov A. S., Zhukov D. O., Khvatova T. Yu., Andrianova E. G., “Model of forecasting of information events on the basis of the solution of a boundary value problem for systems with memory and self-organization”, J. Commun. Technol. El., 18:2 (2018), 106–117 |
16. |
Zhukov D., Khvatova T., Istratov L., “A stochastic dynamics model for shaping stock indexes using self-organization processes, memory and oscillations”, European Conference on the Impact of Artificial Intelligence and Robotics Proceedings, ACPIL, Oxford, U.K., 2019, 390–401 |
17. |
Zhukov D., Zaltsman A., Khvatova T., “Forecasting changes in states in social networks and sentiment security using the principles of percolation theory and stochastic dynamics”, IEEE Conference (International) “Quality Management, Transport and Information Security, Information Technologies”, IEEE, Piscataway, NJ, USA, 2019, 149–153 |
18. |
Smychkova A., Zhukov D., “Complex of description models for analysis and control group behavior based on stochastic cellular automata with memory and systems of differential kinetic equations”, 1st Conference (International) on Control Systems, Mathematical Modelling, Automation and Energy Efficiency Proceedings, Lipetsk State Technical University, Lipetsk, 2019, 218–223 |
19. |
Zhukov D., Khvatova T., Millar C., Zaltsman A., “Modeling the stochastic dynamics of influence expansion and managing transitions between states in social networks”, Technol. Forecast. Soc., 158 (2020), 1–15 |
20. |
Жуков Д. О., Хватова Т. Ю., Зальцман А. Д., “Моделирование стохастической динамики изменения состояний узлов и перколяционных переходов в социальных сетях с учетом самоорганизации и наличия памяти”, Информатика и её применения, 15:1 (2021), 102–110 [Zhukov D. O., T. Yu. Khvatova, A. D. Zaltcman, “Modeling of the stochastic dynamics of changes in node states and percolation transitions in social networks with self-organization and memory”, Informatika i ee Primeneniya — Inform. Appl., 15:1 (2021), 102–110] |
21. |
Zhukov D., Andrianova E., Trifonova O., “Stochastic diffusion model for analysis of dynamics and forecasting events in news feeds”, Symmetry, 13:2 (2021), 257, 21 pp. |
22. |
Zhukov D., Andrianova E., Novikova O., “Diffusion model for forecasting events in news feeds”, J. Phys. Conf. Ser., 1727:1 (2021), 21–32 |