Methodology for creating, clustering and visualizing correlation networks determined by the dynamics of thematic information flows

Authors

  • Oleksandr Puchkov Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0002-8585-1044
  • Dmytro Lande Educational and scientific physico-technical institute at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0003-3945-1178
  • Ihor Subach Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0002-9344-713X

DOI:

https://doi.org/10.20535/2411-1031.2025.13.1.328753

Keywords:

correlation networks, thematic information flows, clustering, visualization, dynamics vectors, content monitoring, cybersecurity, Gephi, Ph-Di diagram, semantic networks

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.

Author Biographies

Oleksandr Puchkov, Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

PhD in philosophy, professor, head

Dmytro Lande, Educational and scientific physico-technical institute at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

doctor of technical sciences, professor, chair of the academic department of the information security

Ihor Subach, Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

doctor of technical sciences, professor, chair of the academic department of the cyber security and application of information systems and technologies

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Published

2025-05-20

How to Cite

Puchkov, O., Lande, D., & Subach, I. (2025). Methodology for creating, clustering and visualizing correlation networks determined by the dynamics of thematic information flows. Collection "Information Technology and Security", 13(1), 6–16. https://doi.org/10.20535/2411-1031.2025.13.1.328753

Issue

Vol. 13 No. 1 (2025)

Section

INFORMATION TECHNOLOGY

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