Comparative analysis of centrality measures for identifying key agents in the network of regional marketing communities

 
Audio is AI-generated
128

Abstract

The article presents a comparative analysis of basic centrality measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, PageRank, and Katz centrality) to identify influential agents in the network of regional marketing communities. The study is based on data from marketing communities in Arkhangelsk, collected through the API of the «VKontakte» platform. The analysis revealed that the network has a pronounced cluster structure and contains hub nodes that ensure its connectivity. Based on the centrality distributions, an assessment of the resilience of the marketing community network was conducted, identifying groups of key nodes that play a significant role in information dissemination.

General Information

Keywords: social influence, centrality measures, network analysis, marketing communities, network resilience analysis

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex topics: Digital Marketing and Social Media, Business Strategy and Innovation, Innovation Diffusion and Forecasting

Information about the work in OpenAlex

Number of citations: 0

Topics

Digital Marketing and Social Media

This cluster of papers explores the impact of social media on consumer behavior, focusing on electronic word-of-mouth, consumer engagement, online reviews, brand communities, influencer marketing, customer relationship management, online branding, user-generated content, and marketing analytics. It delves into the motivations and consequences of consumer interactions in virtual communities and the role of social media in shaping brand perceptions and purchase intentions.

Number of works: 162134  |  Total number of citations: 2097961

Topic detailsв OpenAlex

Business Strategy and Innovation

This cluster of papers explores the concept of 'coopetition' - a strategy where entities cooperate and compete simultaneously within business networks. The focus is on innovation, collaboration with competitors, value creation, and managing tensions in coopetitive relationships. The papers discuss the implications of coopetition for small and medium-sized enterprises, technological innovation, strategic alliances, and knowledge sharing.

Number of works: 94907  |  Total number of citations: 1340557

Topic detailsв OpenAlex

Innovation Diffusion and Forecasting

This cluster of papers focuses on the models, dynamics, and factors influencing the diffusion of technology and innovation across different markets and countries. It explores concepts such as innovation diffusion, agent-based modeling, market penetration, global technology spillover, forecasting models like S-curves and long-wave theory, and the impact of technological paradigms on adoption.

Number of works: 29084  |  Total number of citations: 521243

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2025150201

Received 18.03.2025

Revised 21.03.2025

Accepted

Published

For citation: Antonov, A.V., Stirmanova, R.S. (2025). Comparative analysis of centrality measures for identifying key agents in the network of regional marketing communities. Modelling and Data Analysis, 15(2), 7–26. (In Russ.). https://doi.org/10.17759/mda.2025150201

© Antonov A.V., Stirmanova R.S., 2025

License: CC BY-NC 4.0

References

  1. Berger, J., Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49, № 2, 192-205.
  2. Bonacich, P. F. (1987) Power and Centrality: A Family of Measures. American Journal of Sociology, Vol. 92, 1170-1182.
  3. Brandes, U. A (2001). Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology, Vol. 25, № 2, 163-177.
  4. Chevalier, J. A., Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, Vol. 43, № 3, 345-354.
  5. Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, Vol. 1, № 3, 215-239.
  6. Iyengar, R., Van den Bulte, C., Valente, T. W. (2011). Opinion Leadership and Social Contagion in New Product Diffusion. Marketing Science, Vol. 30, № 2, 195-212.
  7. Jackson, M. (2010). Social and Economic Networks. Princeton: Princeton University Press, 520 p.
  8. Latora, V., Marchiori, M. (2001). Efficient Behavior of Small-World Networks. Physical Review Letters, Vol. 87, № 19, 198701.
  9. Milgram, S., Bickman, L., Berkowitz, L. (1969). Note on the Drawing Power of Crowds of Different Size. Journal of Personality and Social Psychology, Vol. 13, № 2, 79-82.
  10. Newman, M. (2010). Networks. Oxford: Oxford University Press.
  11. Opsahl, T., Agneessens, F., Skvoretz, J. (2010). Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Social Networks, Vol. 32, № 3, 245-251.
  12. Padgett, J. F., Ansell, C. K. (1993). Robust Action and the Rise of the Medici, 1400–1434. American Journal of Sociology, Vol. 98, № 6, 1259-1319.
  13. Wei, D., Li, Y., Zhang, Y., Deng, Y. (2012). Degree Centrality Based on the Weighted Network. Control and Decision Conference (CCDC), IEEE, 3976-3979.
  14. Волков, Д. В., Саенко, И. Б., Старков А. М., Султанбеков, А. Т. (2018). Оценка устойчивости сети передачи данных в условиях деструктивных воздействий. Известия Тульского государственного университета. Технические науки, № 12, 358-363. 
    Volkov, D. V., Saenko, I. B., Starkov, A. M., Sultanbekov, A. T. (2018). Assessment of network resilience under destructive impacts. Izvestiya TulGU. Technical Sciences, No. 12, 358–363.
  15. Гегель, Л. А., Бабочкина, С. П. (2010). Влияние социальных факторов на выбор профессии учащихся старших классов общеобразовательных школ. Социально-гуманитарные знания, № 5, 79-85. 
    Gegel, L. A., Babochkina, S. P. Influence of social factors on career choice of high school students. Social and Humanitarian Knowledge, No. 5, 79–85.
  16. Дьяконов, А. Г. (2012). Алгоритмы для рекомендательной системы: технология LENKOR. Бизнес-информатика, № 1(19), 32-39. 
    Dyakonov, A. G. (2012). Algorithms for recommendation systems: LENKOR technology. Business Informatics, No. 1(19), 32–39.
  17. Полякова, О. С., Подлесный, А. О. (2013). PageRank. Алгоритм ссылочного ранжирования. Наука и современность, № 20, 154-157. 
    Polyakova, O. S., Podlesny, A. O. (2013). PageRank: Link ranking algorithm. Science and Modernity, No. 20, 154–157.
  18. Утакаева, И. Х. (2020). Моделирование распространения инфекционных заболеваний в социальных сетях. Самоуправление, Т. 2, № 2(119), 559-564. 
    Utakaeva, I. Kh. (2020). Modeling the spread of infectious diseases in social networks. Self-Governance, Vol. 2, No. 2(119), 559–564.
  19. Харари, Ф. (2003). Теория графов : пер. с англ.; ред. Гаврилов Г. П. ; пер. Козырев В. П. – 2-е изд. – М. : Едиториал УРСС, 300 с. : ил. – Библиогр.: с. 268-286. 
    Harary, F. (2003). Graph Theory : Transl. from English; by V. P. Kozyrev; edited by G. P. Gavrilov. – 2nd ed. – Moscow: Editorial URSS, 300 p. – Bibliography: Pp. 268–286.
  20. Чижова, Л.А., Тутыгин, А. Г., Стирманова, Р. С. (2024). Сетевые молодежные сообщества в социокультурном пространстве северного региона: методологические и эмпирические аспекты исследования. Вестник Российского университета дружбы народов. Серия: Социология, Т. 24, №4, 1033-1051.
    Chizhova, L.A., Tutygin, A.G., Stirmanova, R.S. (2024). Network Youth Communities in the Socio-Cultural Space of the Northern Region: Methodological and Empirical Aspects of the Study. Bulletin of the Russian University of Friendship of Peoples. Series: Sociology, Vol. 24, No. 4, 1033-1051.
  21. Центральность в социальных сетях [Электронный ресурс] // CentiServer. – URL: https://centiserver.ir/centrality/list/ (дата обращения: 20.12.2024).
    Centrality in Social Networks [Electronic resource] // CentiServer. – Available at: https://centiserver.ir/centrality/list/ (accessed: 20.12.2024).

Information About the Authors

Anatoliy V. Antonov, Master's student at the Department of Higher and Applied Mathematics, Northern (Arctic) Federal University named after M. V. Lomonosov, Arkhangelsk, Russian Federation, ORCID: https://orcid.org/0009-0009-0187-2410, e-mail: s3519008@edu.narfu.ru

Raisa S. Stirmanova, PhD student at the Department of Higher and Applied Mathematics, Northern (Arctic) Federal University named after M. V. Lomonosov, Arkhangelsk, Russian Federation, ORCID: https://orcid.org/0000-0001-9819-0890, e-mail: r.s.stirmanova@gmail.com

Contribution of the authors

Raisa S. Stirmanova — ideas; annotation, writing and design of the manuscript; planning of the
research; control over the research.

Anatoliy V. Antonov — application of statistical, mathematical or other methods for data analysis;
conducting the experiment; data collection and analysis; visualization of research results.

All authors participated in the discussion of the results and approved the final text of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

Ethics statement

The study was reviewed and approved by the expert commission of the Northern (Arctic) Federal
University named after M.V. Lomonosov (conclusion on the possibility of open publication dated
02/05/2025).

Metrics

 Web Views

Whole time: 335
Previous month: 16
Current month: 0

 PDF Downloads

Whole time: 128
Previous month: 31
Current month: 2

 Total

Whole time: 463
Previous month: 47
Current month: 2