Preview

Administrative Consulting

Advanced search

Opinion Leader Identification for Artificial Intelligence Domain Analysis Using a Graph-Based Model

EDN: PLSPIC

Abstract

This paper addresses the challenge of navigating the rapidly evolving field of artificial intelligence (AI), using large language models as a representative example. It proposes a graph-based representation of the scientific community as an analytical tool for describing the structure of relationships between researchers and identifying research groups. The study also introduces an approach for detecting key figures and opinion leaders within the field. The underlying assumption is that analyzing the publications of such groups can help capture emerging trends in a timely manner and support informed decisions regarding the adoption and implementation of relevant technologies. Using this approach, a graph model was constructed based on open scientometric data: researchers are represented as nodes with additional attributes, while their relationships are encoded as edges. The influence of individual authors was quantified using PageRank centrality, and latent research groups were identified through the Louvain clustering algorithm. The results support the initial hypotheses: scholars with high PageRank scores are indeed recognized industry leaders, and the algorithm consistently identifies five clusters corresponding to real research and corporate structures. Overall, the proposed graph model can be considered a supporting tool for analytical characterization of the current AI research landscape and for monitoring emerging scientific trends.

About the Authors

O. A. Shutko
PJSC «Sberbank of Russia»
Russian Federation

Oleg A. Shutko, Data Scientist Intern 

Saint Petersburg



A. V. Poptsov
Saint-Petersburg Federal Research Center of the Russian Academy of Sciences
Russian Federation

Alexander V. Poptsov, Research Intern 

Saint Petersburg



V. D. Oliseenko
Saint-Petersburg Federal Research Center of the Russian Academy of Sciences
Russian Federation

Valerii D. Oliseenko, Researcher

Saint Petersburg



References

1. Baba V. V., HakemZadeh F. Toward a theory of evidence based decision making // Management Decision. 2012. Vol. 50. N 5. P. 832–867.

2. 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. Article N P10008.

3. Brynjolfsson E., Li D., Raymond L. Generative AI at Work // The Quarterly Journal of Economics. 2025. Vol. 140, N 2. P. 889–942.

4. Choudhary S., Sharma K., Bajaj M. A Review on Opinion Leader Detection and Its Applications // Proceedings of the 4th International Conference on Communication & Information Processing (ICCIP). 2022. 11 p.

5. Constellation Research Inc. Artificial Intelligence 150 2024-2025 // Constellation Research Inc. URL: https://www.constellationr.com/artificial-intelligence-150/2024-2025.

6. Dudkina E., Bin M., Breen J., Crisostomi E., Ferraro P., Kirkland S., Mareček J., Murray-Smith R., Parisini T., Stone L., Yilmaz S., Shorten R. A comparison of centrality measures and their role in controlling the spread in epidemic networks // International Journal of Control. 2024. Vol. 97, N 6. P. 1325–1340.

7. Ding Y., Yan E., Frazho A., Caverlee J. PageRank for ranking authors in co-citation networks // Journal of the American Society for Information Science and Technology. 2009. Vol. 60, N 11. P. 2229–2243.

8. Ding Y., Yan E., Frazho A., Caverlee J. Discovering author impact: a PageRank perspective // Information Processing & Management. 2011. Vol. 47, N 1. P. 125–134.

9. Fonseca B. P. F., Sampaio R. B., Fonseca M. V. de A., Zicker F. Co-authorship network analysis in health research: method and potential use // Health Research Policy and Systems. 2016. Vol. 14. Article N 34. DOI 10.1186/s12961-016-0104-5.

10. Fortunato S., Bergstrom C. T., Börner K., Evans J. A., Helbing D., Milojević S., Petersen A. M., Radicchi F., Sinatra R., Uzzi B., Vespignani A. Science of science // Science. 2018. Vol. 359, N 6379. Article N eaao0185. DOI 10.1126/science.aao0185.

11. Grebe M., Franke M. R., Heinzl A. Artificial intelligence: how leading companies define use cases, scale-up utilization, and realize value // Informatik Spektrum. 2023. Vol. 46. P. 197–209.

12. Jaouadi M., Ben Romdhane L. A survey on influence maximization models // Expert Systems with Applications. 2024. Article N 123429. DOI 10.1016/j.eswa.2024.123429.

13. Jada I., Mayayise T. O. The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review // Data and Information Management. 2024. Vol. 8, N 2. Article N 100063.

14. Jin B., Zou M., Wei Z., Guo W. How to find opinion leader on the online social network? // Applied Intelligence. 2025. Vol. 55. Article N 624.

15. Katz E. The two-step flow of communication: An up-to-date report on an hypothesis // Public Opinion Quarterly. 1957. Vol. 21, N 1. P. 61–78.

16. Lu G., Guo X., Zhang R., Zhu W., Liu J. BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs. 2025. Manuscript.

17. Maslov S., Redner S. Promise and pitfalls of extending Google’s PageRank algorithm to citation networks // Journal of Neuroscience (J Neurosci). 2008. Vol. 28, N 44. P. 11103–11105.

18. Minaee Sh., Mikolov T., Nikzad N., Chenaghlu M., Socher R., Amatriain X., Gao J. Large Language Models: A Survey. 2024. Preprint.

19. Naveed H., Khan A. U., Qiu S., Saqib M., Anwar S., Usman M., Akhtar N., Barnes N., Mian A. A comprehensive overview of large language models // ACM Transactions on Intelligent Systems and Technology. 2025. Vol. 16, N 5. P. 1–72.

20. OpenAlex. (n.d.). Bibliographic catalog of scientific articles. Retrieved from https://openalex.org/ Priem J., Piwowar H., Orr R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts.

21. Process Excellence Network. The Top 30 AI Leaders in PEX to Follow in 2025 // Process Excellence Network. URL: https://www.processexcellencenetwork.com/ai/articles/the-top-30-ai-leaders-in-pex-to-follow-in-2025.

22. Razis G., Anagnostopoulos I., Zeadally S. Modeling influence with semantics in social networks: a survey. Manuscript. University of Thessaly; University of Kentucky.

23. Sharma K., Bajaj M. A review on opinion leader detection and its applications // Proceedings of the 2022 International Conference on Communication and Electronics Systems (ICCES). 2022. P. 1645–1651. IEEE.

24. Sugimoto C. R., Work S., Larivière V., Haustein S. Scholarly use of social media and altmetrics: a review of the literature // Journal of the Association for Information Science and Technology. 2017. Vol. 68, N 9. P. 2037–2062. DOI 10.1002/asi.23833.

25. Weidinger L., Mellor J., Rauh M., Griffin C., Uesato J., Huang P.-S., Cheng M., Glaese M., Balle B., Kasirzadeh A., Kenton Z., Brown S., Hawkins W., Stepleton T., Biles C., Birhane A., Haas J., Rimell L., Hendricks L. A., Isaac W., Legassick S., Irving G., Gabriel I. Ethical and social risks of harm from Language Models // arXiv.org. 2021. Preprint, arXiv:2112.04359.

26. TIME. The 100 Most Influential People in AI 2024 // TIME. 2024. URL: https://time.com/collection/time100-ai-2024/ (accessed: 07.11.2025).

27. TIME. The 100 Most Influential People in AI 2025 // TIME. 2025. URL: https://time.com/collection/time100-ai-2025/ (accessed: 07.11.2025).

28. Xiao Y., Chen Y., Zhang H., Zhu X., Yang Y., Zhu X. A new semi-local centrality for identifying influential nodes based on local average shortest path with extended neighborhood // Artificial Intelligence Review. 2024. Vol. 57. Article N 115.

29. Xie Y., Meisel J. D., Meisel C. A., Betancourt J. J., Yan J., Bugiolacchi R. Spotting leaders in organizations with graph convolutional networks, explainable artificial intelligence, and automated machine learning // Applied Sciences. 2024. Vol. 14, N 20. Article N 9461. DOI 10.3390/app14209461.

30. Xu Q., Sun L., Bu C. The two-steps eigenvector centrality in complex networks // Chaos, Solitons & Fractals. 2023. Vol. 173. Article N 113753.

31. Yanchenko E., Murata T., Holme P. Influence maximization on temporal networks: a review // Applied Network Science. 2024. Vol. 9. Article N 16.


Review

For citations:


Shutko O.A., Poptsov A.V., Oliseenko V.D. Opinion Leader Identification for Artificial Intelligence Domain Analysis Using a Graph-Based Model. Administrative Consulting. 2025;(6):111-120. (In Russ.) EDN: PLSPIC

Views: 14


ISSN 1726-1139 (Print)
ISSN 1816-8590 (Online)