Methodology for creating, clustering and visualizing correlation networks determined by the dynamics of thematic information flows
Year: 2025
Type: article
Abstract: Given the rapid growth of information circulating in social media and the Internet space, there is an urgent need for effective methods of analyzing and visualizing thematic information flows. Correlation networks are a powerful tool for formalizing such processes, as they allow identifying relationships between different objects, including by analyzing their dynamics. This is especially relevant for the cybersecurity sector, where prompt detection of trends and connections between events can be crucial. The article is devoted to the development of a methodology for creating, clustering and visualizing correlation networks determined by the dynamics of thematic information flows. The article proposes an approach based on the analysis of vectors of publication dynamics obtained through social media content monitoring systems. Correlation networks are formed based on relationships between vectors reflecting the distribution of documents by dates. To visualize and analyze the networks, tools such as Gephi are used, as well as the author's own Ph-Di diagram to display the dynamics of information flows. The methodology allows identifying groups of interconnected objects, which can be useful for analyzing thematic information flows, particular in the field of cybersecurity. The results of the study can serve as a basis for building probabilistic networks and further scenario analysis. The advantages of the proposed methodology are the low dimensionality of the vectors, which simplifies their processing and analysis, language independence, so that the methodology can be used to analyze information flows in different languages, and ease of implementation, which makes it accessible to a wide range of researchers and analysts in the field of cybersecurity.
Authors: Oleksandr Puchkov, Dmytro Lande, Ihor Subach
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413064075