kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-2-37-53kaz29-1985Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEРАЗРАБОТКА ПРИКЛАДНОГО ПОДХОДА К МОДЕЛИРОВАНИЮ ИНФОРМАЦИОННОГО ПРОТИВОСТОЯНИЯ В СОЦИАЛЬНЫХ СЕТЯХ НА ОСНОВЕ МЕТОДОВ ТЕОРИИ ИГРDEVELOPMENT OF A PRACTICAL APPROACH FOR INFORMATION CONFRONTATION MODELING IN SOCIAL NETWORKS BASED ON GAME THEORY METHODShttps://orcid.org/0000-0001-8389-2953МолдабаевД. А.MoldabayevD. A.

 магистрант 

 г. Алматы 

 Master’s student 

 Almaty 

d_moldabayev@kbtu.kzhttps://orcid.org/0000-0002-5503-0077ТиналМ. Б.TinalM. B.

 магистрант 

 г. Алматы 

 Master’s student 

 Almaty 

m_tinal@kbtu.kzhttps://orcid.org/0000-0003-0592-5865КартбаевА. Ж.KartbayevA. Zh.

 PhD 

 г. Алматы 

 PhD 

 Almaty 

a.kartbayev@gmail.comКазахстанско-Британский технический университетКазахстанKazakh-British Technical UniversityKazakhstan2025060720252223753Copyright © Молдабаев Д.А., Тинал М.Б., Картбаев А.Ж., 20252025Молдабаев Д.А., Тинал М.Б., Картбаев А.Ж.Moldabayev D.A., Tinal M.B., Kartbayev A.Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1985

В данной работе исследуется динамика социальных сетей в контексте информационного противоборства между пользователями. В работе представлен новый способ моделирования информационного противоборства в социальных сетях, основанный на теоретико-игровых и вероятностных подходах. Кроме того, в статье предлагается метод динамического наблюдения, отслеживания и обновления состояния сети. Этот инновационный метод концептуализирует информационные конфликты как игру для двух игроков, целью которой является контроль как можно большего числа узлов сети. Применяя теорию игр, мы разработали эффективный алгоритм адаптации стратегий, который позволяет каждому игроку модифицировать свое принятие решений на основе открытого набора данных Facebook Researcher (а именно его казахстанского сегмента) и текущих условий сети. Метод динамического отслеживания состояния сети, представленный в данном исследовании, приводит к значительному снижению использования ресурсов и улучшению вычислительной эффективности. Сравнительные вычислительные тесты с другими методологиями демонстрируют практическую ценность нашего подхода. Гибкость и эффективность предложенного метода делают его перспективным инструментом для решения широкого спектра задач в информационных и аналитических системах.

This study investigates the dynamics of social networks in the context of information confrontation between users. It introduces a simulation method for modeling these conflicts, which is based on game-theoretic and probabilistic approaches. The paper suggests a method for dynamically observing, following, and updating the status of the network. This innovative method conceptualizes information conflicts as a two-player game where the objective is to control as many network nodes as possible. By applying game theory, we formulated a strategy adaptation algorithm that allows each player to modify their decision-making based on the Facebook Researcher open dataset and current network conditions of its Kazakhstani segment. The method for tracking the network’s state dynamically leads to significant reductions in resource use and enhancements in computational efficiency. Comparative computational tests against other methodologies demonstrate the practical value of our approach for addressing a broad spectrum of challenges in information and analytical systems.

теория иградаптация стратегийсоциальные сетиинформационный конфликталгоритм моделированиявероятностный подходаналитические системыgame theorystrategy adaptationSocial networksinformation conflictsimulation algorithmprobabilistic approachanalytical systemsReferencesWang J., Yang Y., Liu Q., Fang Z., Sun S., Xu Y. An empirical study of user engagement in influencer marketing on Weibo and WeChat // IEEE Transactions on Computational Social Systems. – 2023. – Vol. 10. – P. 3228–3240. https://doi.org/10.1109/TCSS.2022.3204177.Wang J., Yang Y., Liu Q., Fang Z., Sun S., Xu Y. An empirical study of user engagement in influencer marketing on Weibo and WeChat // IEEE Transactions on Computational Social Systems. – 2023. – Vol. 10. – P. 3228–3240. https://doi.org/10.1109/TCSS.2022.3204177.Sun B., Al-Bayaty R., Huang Q., Wu D.O. Game theoretical approach for non-overlapping community detection // Proceedings of the 5th International Conference on Big Data Computing and Communications. – 2019. – P. 222–230. https://doi.org/10.26599/TST.2020.9010017.Sun B., Al-Bayaty R., Huang Q., Wu D.O. Game theoretical approach for non-overlapping community detection // Proceedings of the 5th International Conference on Big Data Computing and Communications. – 2019. – P. 222–230. https://doi.org/10.26599/TST.2020.9010017.Bruning P.F., Alge B.J., Lin H.-C. Social networks and social media: Understanding and managing influence vulnerability in a connected society // Business Horizons. – 2020. – Vol. 63. – No. 6. – P. 749–761. https://doi.org/10.1016/j.bushor.2020.07.007.Bruning P.F., Alge B.J., Lin H.-C. Social networks and social media: Understanding and managing influence vulnerability in a connected society // Business Horizons. – 2020. – Vol. 63. – No. 6. – P. 749–761. https://doi.org/10.1016/j.bushor.2020.07.007.Huang D., Tan X., Chen N., Fan Z. A memetic algorithm for solving the robust influence maximization problem on complex networks against structural failures // Sensors. – 2022. – Vol. 22. – No. 6. – P. 2191. https://doi.org/ 10.3390/s22062191.Huang D., Tan X., Chen N., Fan Z. A memetic algorithm for solving the robust influence maximization problem on complex networks against structural failures // Sensors. – 2022. – Vol. 22. – No. 6. – P. 2191. https://doi.org/ 10.3390/s22062191.Peng Y., Bai X. Identifying social tipping point through perceived peer effect // Environmental Innovation and Societal Transitions. – 2024. – Vol. 51. – P. 2367–2382. https://doi.org/10.1016/j.eist.2024.100847.Peng Y., Bai X. Identifying social tipping point through perceived peer effect // Environmental Innovation and Societal Transitions. – 2024. – Vol. 51. – P. 2367–2382. https://doi.org/10.1016/j.eist.2024.100847.Liu Y., Bao Z., Zhang Z., Tang D., Xiong F. Information cascades prediction with attention neural network // Human-centric Computing and Information Sciences. – 2020. – Vol. 10. https://doi.org/10.1186/s13673-020-00218-w.Liu Y., Bao Z., Zhang Z., Tang D., Xiong F. Information cascades prediction with attention neural network // Human-centric Computing and Information Sciences. – 2020. – Vol. 10. https://doi.org/10.1186/s13673-020-00218-w.Rangnani S., Devi V.S. Predicting potential retweeters for a microblog on Twitter // Intelligent and Evolutionary Systems. – 2016. – Vol. 5. – P. 1–5. https://doi.org/10.1007/978-3-319-27000-5_14.Rangnani S., Devi V.S. Predicting potential retweeters for a microblog on Twitter // Intelligent and Evolutionary Systems. – 2016. – Vol. 5. – P. 1–5. https://doi.org/10.1007/978-3-319-27000-5_14.Qiang Z., Pasiliao E., Zheng Q. Model-based learning of information diffusion in social media networks // Applied Network Science. – 2019. – Vol. 4. – No. 1. – P. 1–16. https://doi.org/10.1007/s41109-019-0215-3.Qiang Z., Pasiliao E., Zheng Q. Model-based learning of information diffusion in social media networks // Applied Network Science. – 2019. – Vol. 4. – No. 1. – P. 1–16. https://doi.org/10.1007/s41109-019-0215-3.Zhang L., Li K., Liu J. An information diffusion model based on explosion shock wave theory on online social networks // Applied Sciences. – 2021. – Vol. 11. – P. 9996. https://doi.org/10.3390/app11219996.Zhang L., Li K., Liu J. An information diffusion model based on explosion shock wave theory on online social networks // Applied Sciences. – 2021. – Vol. 11. – P. 9996. https://doi.org/10.3390/app11219996.Bourigault S., Lagnier C., Lamprier S., Denoyer L., Gallinari P. Learning social network embeddings for predicting information diffusion // Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM ‘14). – 2014. – P. 393–402. https://doi.org/10.1145/2556195.2556216.Bourigault S., Lagnier C., Lamprier S., Denoyer L., Gallinari P. Learning social network embeddings for predicting information diffusion // Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM ‘14). – 2014. – P. 393–402. https://doi.org/10.1145/2556195.2556216.Newman M.E.J. The structure and function of complex networks // SIAM Review. – 2003. – Vol. 45. – P. 167–256. https://doi.org/10.1137/S00361445034248.Newman M.E.J. The structure and function of complex networks // SIAM Review. – 2003. – Vol. 45. – P. 167–256. https://doi.org/10.1137/S00361445034248.Tsai J., Nguyen T., Tambe M. Security games for controlling contagion // Proceedings of the AAAI Conference on Artificial Intelligence. – 2021. – Vol. 26. – No. 1. – P. 1464–1470. https://doi.org/10.1609/aaai.v26i1.8249.Tsai J., Nguyen T., Tambe M. Security games for controlling contagion // Proceedings of the AAAI Conference on Artificial Intelligence. – 2021. – Vol. 26. – No. 1. – P. 1464–1470. https://doi.org/10.1609/aaai.v26i1.8249.Li Q., Du H., Li X.-Y. Influence of influence on social networks: Information propagation causes dynamic networks // Proceedings of the 2021 7th International Conference on Big Data Computing and Communications (BigCom). – 2021. – P. 278–285. https://doi.org/10.1109/BigCom53800.2021.00016.Li Q., Du H., Li X.-Y. Influence of influence on social networks: Information propagation causes dynamic networks // Proceedings of the 2021 7th International Conference on Big Data Computing and Communications (BigCom). – 2021. – P. 278–285. https://doi.org/10.1109/BigCom53800.2021.00016.Podlipskaia O. Determining effective strategies for information warfare in consolidated and polarized populations // Proceedings of the IEEE Conference on Machine Learning and Systems. – 2022. – P. 1–5. https://doi.org/10.1109/MLSD55143.2022.9934658.Podlipskaia O. Determining effective strategies for information warfare in consolidated and polarized populations // Proceedings of the IEEE Conference on Machine Learning and Systems. – 2022. – P. 1–5. https://doi.org/10.1109/MLSD55143.2022.9934658.Li T., Zhao Y., Zhu Q. The role of information structures in game-theoretic MAL // Annual Reviews in Control. – 2022. – Vol. 53. – P. 296–314. https://doi.org/10.1016/j.arcontrol.2022.03.003.Li T., Zhao Y., Zhu Q. The role of information structures in game-theoretic MAL // Annual Reviews in Control. – 2022. – Vol. 53. – P. 296–314. https://doi.org/10.1016/j.arcontrol.2022.03.003.Ganai A.H., Hashmy R., Khanday H.A. Finding information diffusion’s seed nodes in online social networks using a special degree centrality // SN Computer Science. – 2024. – Vol. 5. – P. 333. https://doi.org/10.1007/s42979-024-02683-x.Ganai A.H., Hashmy R., Khanday H.A. Finding information diffusion’s seed nodes in online social networks using a special degree centrality // SN Computer Science. – 2024. – Vol. 5. – P. 333. https://doi.org/10.1007/s42979-024-02683-x.Genschow O., Klomfar S., d’Haene I., Brass M. Mimicking and anticipating others’ actions is linked to social information processing // PLoS One. – 2018. – Vol. 13. – No. 3. – e0193743. https://doi.org/10.1371/journal.pone.0193743.Genschow O., Klomfar S., d’Haene I., Brass M. Mimicking and anticipating others’ actions is linked to social information processing // PLoS One. – 2018. – Vol. 13. – No. 3. – e0193743. https://doi.org/10.1371/journal.pone.0193743.Papadopoulou O., Makedas T., Apostolidis L., Poldi F., Papadopoulos S., Kompatsiaris I. MeVer NetworkX: Network analysis and visualization for tracing disinformation // Future Internet. – 2022. – Vol. 14. – No. 5. – P. 147. https://doi.org/10.3390/fi14050147.Papadopoulou O., Makedas T., Apostolidis L., Poldi F., Papadopoulos S., Kompatsiaris I. MeVer NetworkX: Network analysis and visualization for tracing disinformation // Future Internet. – 2022. – Vol. 14. – No. 5. – P. 147. https://doi.org/10.3390/fi14050147.Iqbal S., Arif T., Malik M., Sheikh A. Browser simulation-based crawler for online social network profile extraction // International Journal of Web Based Communities. – 2020. – Vol. 16. – P. 321–342. https://doi.org/10.1504/IJWBC.2020.111377.Iqbal S., Arif T., Malik M., Sheikh A. Browser simulation-based crawler for online social network profile extraction // International Journal of Web Based Communities. – 2020. – Vol. 16. – P. 321–342. https://doi.org/10.1504/IJWBC.2020.111377.O’Neil D., Petty M. Heuristic methods for synthesizing realistic social networks based on personality compatibility // Applied Network Science. – 2019. – Vol. 4. – P. 141–158. https://doi.org/10.1007/s41109-019-0117-4.O’Neil D., Petty M. Heuristic methods for synthesizing realistic social networks based on personality compatibility // Applied Network Science. – 2019. – Vol. 4. – P. 141–158. https://doi.org/10.1007/s41109-019-0117-4.Luo T., Cao Z., Zeng D., Zhang Q. A dissemination model based on psychological theories in complex social networks // IEEE Transactions on Cognitive and Developmental Systems. – 2021. – Vol. 14. – No. 2. – P. 519–531. https://doi.org/10.1109/TCDS.2021.3052824.Luo T., Cao Z., Zeng D., Zhang Q. A dissemination model based on psychological theories in complex social networks // IEEE Transactions on Cognitive and Developmental Systems. – 2021. – Vol. 14. – No. 2. – P. 519–531. https://doi.org/10.1109/TCDS.2021.3052824.Moscato V., Picariello A., Sperlí G. Community detection based on game theory // Engineering Applications of Artificial Intelligence. – 2019. – Vol. 85. – P. 773–782. https://doi.org/10.1016/j.engappai.2019.08.003.Moscato V., Picariello A., Sperlí G. Community detection based on game theory // Engineering Applications of Artificial Intelligence. – 2019. – Vol. 85. – P. 773–782. https://doi.org/10.1016/j.engappai.2019.08.003.Plekhanov D., Franke H., Netland T.H. Digital transformation: A review and research agenda // European Management Journal. – 2023. – Vol. 41. – No. 6. – P. 821–844. https://doi.org/10.1016/j.emj.2022.09.007.Plekhanov D., Franke H., Netland T.H. Digital transformation: A review and research agenda // European Management Journal. – 2023. – Vol. 41. – No. 6. – P. 821–844. https://doi.org/10.1016/j.emj.2022.09.007.

The authors declare that there are no conflicts of interest present.