Vavryk P. Models and methods for analyzing evolutionary processes of information transmission in social networks

The PhD thesis is dedicated to the research and formalization of evolutionary processes of information propagation in social networks, based on the construction of effective models for representing complex networks and modeling interactions between entities in complex networks. With the rapid development of information technologies and the general increase in the size of technological processes, there is a growing need to conduct systematic research of such complex systems. During research, scientists most often face two fundamental problems: collecting relevant data about systems and building mathematical models to describe the processes occurring within them. In the case of simple systems, the mathematical apparatus has already been formed, but as soon as the system begins to exhibit dynamic characteristics, the existing mathematical apparatus is not able to provide researchers with up-to-date information about the state of the system. Frequently, interdisciplinary approaches based on separate methods of physics, biology, and sociology are used to formalize complex systems: the theory of complex networks, graph theory, chaos theory, cellular automata, information theory, game theory, diffusion models, fractal analysis, time series analysis, differential equations, partial differential equations, difference equations. However, with the growing complexity of systems and the need for their complex analysis, agent-based modeling methods are increasingly applied. The work of Albert-László Barabási, Stephen Wolfram, Stuart Kauffman, Robert Axtell, Joshua M. Epstein and others is devoted to the study of complex systems. It is worth noting that complex systems are usually differ significantly and depend on the modeling domain.