mireabulletinRussian Technological JournalRussian Technological Journal2782-32102500-316XRTU MIREA10.32362/2500-316X-2023-11-2-33-49mireabulletin-653Research ArticleИНФОРМАЦИОННЫЕ СИСТЕМЫ. ИНФОРМАТИКА. ПРОБЛЕМЫ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИINFORMATION SYSTEMS. COMPUTER SCIENCES. ISSUES OF INFORMATION SECURITYМодели и методы анализа сложных сетей и социальных сетевых структурModels and methods for analyzing complex networks and social network structureshttps://orcid.org/0000-0003-4028-2842ПероваЮ. П.PerovaJu. P.

Перова Юлия Петровна, старший преподаватель кафедры телекоммуникаций Института радиоэлектроники и информатики 

Scopus Author ID 57431908700

119454, Москва, пр-т Вернадского, д. 78

Julia P. Perova, Senior Lecturer, Department of Telecommunications, Institute of Radio Electronics and Informatics

Scopus Author ID 57431908700

78, Vernadskogo pr., Moscow, 119454 

perova_yu@mirea.ruГригорьевВ. Р.GrigorievV. P.

Григорьев Виталий Робертович, кандидат технических наук, доцент, заведующий кафедрой «Информационное противоборство» Института кибербезопасности и цифровых технологий 

119454, Москва, пр-т Вернадского, д. 78

 

Vitaly P. Grigoriev, Cand. Sci. (Eng.), Associate Professor, Head of the Department of Information Warfare, Institute for Cybersecurity and Digital Technologies

78, Vernadskogo pr., Moscow, 119454 

grigorev@mirea.ruЖуковД. О.ZhukovD. O.

Жуков Дмитрий Олегович, доктор технических наук, профессор, профессор кафедры «Информационное противоборство» Института кибербезопасности и цифровых технологий 

Scopus Author ID 57189660218

119454, Москва, пр-т Вернадского, д. 78

Dmitry O. Zhukov, Dr. Sci. (Eng.), Professor, Department of Information Warfare, Institute for Cybersecurity and Digital Technologies

Scopus Author ID 57189660218

78, Vernadskogo pr., Moscow, 119454 

zhukov_do@mirea.ruМИРЭА – Российский технологический университетРоссияMIREA – Russian Technological UniversityRussian Federation2023070420231123349Copyright © Перова Ю.П., Григорьев В.Р., Жуков Д.О., 20232023Перова Ю.П., Григорьев В.Р., Жуков Д.О.Perova J.P., Grigoriev V.P., Zhukov D.O.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rtj-mirea.ru/jour/article/view/653Цели

Цели. Целью статьи является исследование современных моделей и методов анализа сложных социальных сетевых структур и применяемых для этого инструментов, как на основе готовых решений в виде сервисов и программного обеспечения, так и средств разработки собственных приложений с использованием языка программирования Python. Такие исследования позволяют прогнозировать не только динамику общественных процессов (изменение социальных настроений), но и тенденции социально-экономического развития за счет мониторинга мнений пользователей по важным экономическим и социальным вопросам на уровне отдельных территориальных образований (районов, поселений небольших городов и т.д.) и регионов.

Методы

Методы. Рассмотрены и подробно описаны динамические модели и методы анализа стохастической динамики изменения состояний, учитывающие процессы самоорганизации и наличие памяти; методы деанонимизации пользователей; рекомендательные системы; статистические исследования, использующие методы анализа профилей в социальных сетях; методы численного моделирования для анализа сложных сетей и протекающих в них процессов. Особое внимание уделено обработке данных в сложных сетевых структурах средствами языка Python и применению его библиотек.

Результаты

Результаты. Описана специфика решаемых задач при исследовании сложных сетевых структур и их междисциплинарность, связанная с использованием методов системного анализа, теории сложных сетей, текстовой аналитики и компьютерной лингвистики. В частности, исследованы динамические модели процессов, наблюдаемых в сложных социальных сетевых системах, структурные характеристики таких сетей и их взаимосвязь с наблюдаемыми динамическими процессами, в т.ч., с использованием теории построения динамических графов. Исследовано применение нейронных сетей для прогнозирования эволюции динамических процессов, наблюдаемых в сложных социальных системах, и их структуры. Значительное внимание уделено применению методов компьютерной лингвистики, что необходимо для извлечения знаний из текстовых сообщений пользователей социальных сетей при создании моделей, описывающих наблюдаемые процессы.

Выводы

Выводы. Сетевой анализ помогает структурировать модели взаимодействия между социальными единицами: людьми, коллективами, организациями и т.д. По сравнению с другими методами сетевой подход имеет одно неоспоримое преимущество: он позволяет оперировать данными на разных уровнях исследования – от микро- до макроуровня, обеспечивает преемственность этих данных. Установлено, что практически все исследования используют методы работы с текстом, т.к. общение в социальных сетях почти полностью состоит из текстовых сообщений и публикаций. В большинстве исследований используются технологии машинного обучения и искусственного интеллекта. Лучший результат показали сверточные нейронные сети. Из используемых методов также следует выделить метод опорных векторов и дерево решений, т.к. именно они показывали самую высокую точность. Для составления выборок данных и правильного анализа полученных результатов применялись статистические методы.

Objectives

Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.

Methods

Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.

Results

Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.

Conclusions

Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.

социальные сетимоделирование социальных процессовориентированные графымногослойная сверточная нейронная сетькомпьютерная лингвистикакластеризацияsocial networksmodeling of social processesoriented graphsmultilayer convolutional neural networkcomputational linguisticsclusteringИсследование выполнено при поддержке Российского научного фонда, грант № 22-21-00109 «Разработка моделей прогнозирования динамики социальных настроений на основе анализа временных рядов текстового контента социальных сетей с использованием уравнений Фоккера – Планка и нелинейной диффузии».This research was supported by the Russian Science Foundation, grant No. 22-21-00109 “Development of the dynamics forecasting models of social moods based on the analysis of text content time series of social networks using the Fokker–Planck and nonlinear diffusion equations.”ReferencesГубанов Д.А., Новиков Д.А., Чхартишвили А.Г. Социальные сети: модели информационного влияния, управления и противоборства. 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The authors declare that there are no conflicts of interest present.