METHODOLOGY OF CONSTRUCTION AND ANALYSIS OF THE COMMUNICATION NETWORK SOURCE OF INFORMATION
DOI: 10.31673/2786-8362.2024.021324
Abstract
In today's cyberspace, monitoring and analyzing information flows are becoming
critical to preventing cyber threats and detecting disinformation campaigns. Given the increasing number
of cyber threats, an important task is to determine the influence of key information sources and analyze the
links between sources that publish cybersecurity materials. The article proposes a methodology for building
a network of links between information sources based on horizontal visibility graphs with time constraints,
which allows analyzing the thematic similarity and temporal proximity of publications, which makes it
possible to identify key sources of information and determine the likely initiators of information and
cyberattacks. Criteria for establishing links between sources based on thematic similarity and temporal
proximity have been developed, which is key to analyzing the spread of information in real time. An
algorithm for constructing a horizontal visibility graph to form a network of information sources has been
improved, which simultaneously takes into account the thematic similarity and proximity in time of
publications. The use of network metrics, such as centrality and PageRank, is proposed, which allows
assessing the credibility of information sources in the context of specific topics and time intervals, which
significantly improves the understanding of the structure of information flows. The proposed approach not
only helps to identify cyber threats and disinformation campaigns, but also assesses the credibility of
resources, which is important for improving cybersecurity. Experimental results have shown the high
accuracy of the method for identifying key sources and analyzing their role in the dissemination of
information.
Keywords: information flow, disinformation campaign, horizontal visibility graph, network of
connections, thematic similarity, time constraints, credibility of the information source, network building
algorithm, network influence metrics
List of used literature:
1. Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J. C. From time series to complex
networks: The visibility graph // Proceedings of the National Academy of Sciences. 2008. vol. 105,
no 13. P. 4972-4975. DOI: 10.1073/pnas.0709247105.
2. Luque, B., Lacasa, L., Ballesteros, F., Luque, J. Horizontal visibility graphs: Exact results for
random time series // Physical Review E—Statistical, Nonlinear, and Soft Matter Physics. 2009. vol.
80, no 4. DOI: 10.1103/PhysRevE.80.046103.
3. Lande, D. V., Snarskii, A. A., Yagunova, E. V., Pronoza, E. V. The use of horizontal visibility
graphs to identify the words that define the informational structure of a text // 2013 12th Mexican
International Conference on Artificial Intelligence. IEEE, 2013. P. 209-215. DOI:
10.1109/MICAI.2013.33.
4. Donner, R. V., Heitzig, J., Donges, J. F., Zou, Y., Marwan, N., Kurths, J. The geometry of
chaotic dynamics – A complex network perspective. The European Physical Journal B. 2010. vol. 84,
no. 4, P. 653–672. DOI: 10.1140/epjb/e2010-00108-2.
5. Malling, M. Sources that trigger the news: Multiplexity of social ties in news discovery.
Journalism Studies. 2021. vol. 22, no 10. P. 1298-1316. DOI: 10.1080/1461670X.2021.1951331.
6. Kleinberg, J. M. Authoritative sources in a hyperlinked environment. Journal of the ACM
(JACM). 1999. vol. 46, no 5. P. 604-632. DOI: 10.1145/324133.324140.
7. Langville, A. N., Meyer, C. D. Google's PageRank and beyond: The science of search engine
rankings. Princeton university press, 2006. DOI: 10.1515/9781400830329.
8. Sallinen, S., Luo, J., & Ripeanu, M. Real-time pagerank on dynamic graphs // Proceedings of
the 32nd International Symposium on High-Performance Parallel and Distributed Computing. 2023.
С. 239-251. DOI: 10.1145/3588195.3593004.
9. Lande, D., Snarskii, A., Dmytrenko, O., & Subach, I. Relaxation time in complex network //
Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES '20,
2020. pp. 1–6. DOI: 10.1145/3407023.3409231.
10. Dodonov, A., Lande, D., Tsyganok, V., Andriichuk, O., Kadenko, S., Graivoronskaya, A.
Information Operations Recognition. From Nonlinear Analysis to Decision-Making. LAP Lambert
Academic Publishing, 2019. 292 p. ISBN-13: 978-620-0-27697-1.
