Abstract. 

In this paper, the authors present the “Galaxies method” to reveal implicit communities on the graph of interacting objects obtained by importing a network of channels from the Telegram messenger. This method is based on successive identification of overlapping communities on the initial weighted graph, further construction of a new graph, in which the vertices are the communities revealed at the first step, called by the authors “metavertices”. The weighted edges of the new graph between the “metavertices” are built based on the weights between each pair of vertices in the original graph. Further, non-overlapping communities are identified on the new graph. The result is a partition of the original graph into overlapping “meta-communities”. To assess the quality of partitioning using the presented method, the authors carried out a psycholinguistic analysis of the obtained metacommunities, identified patterns depending on the thematic orientation of channels within the metacommunity. As a result of processing and analysis of the obtained metacommunities, the quality of the partition was confirmed. The combination of the algorithmic method of revealing communities and psycholinguistic analysis has a practical application for the analysis of information impact in social networks.

Keywords: 

 

Telegram, analysis of social networks, data import from social networks, model of information impact, graph of interacting objects, identification of communities, psycholinguistic analysis of texts.

PP. 39-50.

DOI: 10.14357/20790279220405

 

References

1. Fortunato S., Newman M.E.J. 20 years of network  community detection. Nat. Phys. 2022. 18. Р. 848–850.

2. Leshchev D.A., Suchkov D.V., Khaykova S.P., Chepovskiy A.A. Algoritms to revel communication groups // Voprosi kiberbezopasnosti. 2019. V. 32. No. 4. P. 61-71

3. Sokolova T.V., Chepovskiy A.A. Analysis on communities profiles in social networks // Highly Available System. 2018. V. 14. No. 3. P. 82-86.

4. Kolomeychenko M.I., Polyakov I.V., Chepovskiy A.A., Chepovskiy A.M. Detection of communities in graph of interactive objects // Fundamentalnaya i Prikladnaya Matematika. 2016. V. 21. No. P. 131-139.

5. Roth M., Ben-David A., Deutscher D. Suggesting Friends Using the Implicit Social Graph — KDD’10. July. 25–28. 2010. Washington. DC. USA. 2010.

6. Girvan M., Newman M. Community structure in social and biological networks // Proceedings of the National Academy of Sciences. 2002. Vol. 99. No. 12. P. 7821-7826.

7. Blondel V.D., Guillaume J.L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks // Journal of Statistical Mechanics: Theory and Experiment. 2008. No. 10. P 10008.

8. Rosvall M. The map equation / M. Rosvall, D. Axelsson, C. T. Bergstrom // The European Physical Journal Special Topics. 2009.

9. Chepovskiy A.A., Leshchev D.A., Khaykova S.P. Core Method for Community Detection / Complex Networks & Their Applications IX. Volume 1: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. Springer. 2021. P. 38-50.

10. Popov V.A., Chepovskiy A.A. Twitter Data Import Models // Vestnik NSU. Series: Information Technologies. 2021. V.19. No. 2. P. 76–91.

11. Popov V.A., Chepovskiy A.A. Telegram Messenger Data Import Models // Vestnik NSU. Series: Information Technologies. 2022. V.20. No. 2. P. 60-71.

12. Kelley S. The existence and discovery of overlapping communities in large-scale networks. Ph.D. thesis, Rensselaer Polytechnic Institute. Troy. NY. 2009.

13. Que X., Checconi F., Petrini F., Gunnels J. Scalable  Community Detection with the Louvain Algorithm // 29th IEEE International Parallel & Distributed Processing Symposium. 2015. May. Р. 25-29.

14. Kolomeychenko M.I., Polyakov I.V., Chepovskiy A.A., Chepovskiy A.M. Metodi vizualnogo analiza graphov. М.: National Open University «INTUIT». 2016. 167 с

15. Avanesyan N.L., Solovev F.N., Chepovskiy A.A. Characteristics of Texts of Social Networks Communities // Vestnik NSU. Series: Information Technologies. 2021. V.19. No. 1. P. 5–14.

16. Fortunato S. & Barthélemy M. Resolution limit in community detection. Proc. Natl. Acad. Sci. USA. 2007. 104. Р. 36-41.