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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Detection of overlap community in social networks founded on game theory

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pp. 1073–1090Vol. 28Issue 6September 2025DOI: 10.47974/JSMS-1415XML
Received:
02 Jul 2024
Published Online:
08 Jul 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1415
Pages:
1073–1090

Abstract

We study a novel approach based on game theory for the identification of overlapping associations and hierarchical structures within a social network. The approach models association detection as a coalition formation game, where individuals in the network act as rational players striving to enhance the overall utility of their groups. To achieve this objective, players collaborate and establish alliances. Each player has the flexibility to join multiple coalitions, and smaller coalitions can merge with one another to create larger ones, as long as the merger leads to an improvement in the coalition’s utility. This approach enables the simultaneous identification of overlapping associations and hierarchical structures in the social network. The utility (service) function of apiece coalition is definite as a grouping of a income function and a price function. The income function events the extent to which the group’s objectives are enhanced, while the cost function counts the level of communication between coalition members and the remaining network. This article investigates two categories of methods based on cooperative and non-cooperative game, and reports the results of tests performed on both artificial and real-life network data. The findings demonstrate that the CoCo-game method and the overlapping method outperform other approaches and result in larger coalitions compared to the clique method and the CoCo-game method. Additionally, the CoCo-game method and the observation method indicate superior performance in identifying associations within clique networks, karate graphs, and Zachary graphs. Notably, in clique graphs, they exhibit exceptional performance of up to 90 percent. In the Zachary graph, they perform well by employing a value of μ = 0.1 for the cross edges section. Furthermore, in networks comprising 10000 nodes and 1000 edges, the CoCo-game method surpasses the clique method in terms of performance and exhibits reduced vertex overlap.

Keywords

Game theoryCoCo-gameCliqueOverlappingDetection of overlapping communitiesSocial networks

Subject Classifications

35Q9105C82

References

[1] A. Lancichinetti, S. Fortunato and J. Kertesz, “Detecting the overlapping and hierarchical community structure in complex networks”,  New Journal of Physics 11 122-128, (2009).
[2] C.H. Chen, “Applying game theory in Newsvendor’s supply chain model”,  Journal of Information and Optimization Sciences, 42(6), 1367-1382, (2021).
[3] K.D. Gupta, A.Q. Ansari, P. Saxena and R. Dwivedi, “Optimizing channel allocation in WiMAX MIMO model using fuzzy game theory”  Journal of Information and Optimization Sciences, 41(7), 1633-1644 (2020).
[4] C. Alós-Ferrer and A.B. Ania, “Ltextocal equilibria in economic games”,  Economics Letters, 70(2), 165-173 (2001).
[5] J. Chen and B. Yuan, “Detecting functional modules in the yeast protein–protein interaction network”,  Bioinformatics, 22(18), 2283-2290 (2006).
[6] P. O. Fjällström, “Algorithms for graph partitioning: A survey”,  Linköping University Electronic Press, (1998).
[7] M.E. Newman, “Who is the best connected scientist? A study of scientific coauthorship networks”,  Complex networks (pp. 337-370). Berlin, Heidelberg: Springer Berlin Heidelberg (2004).
[8] M.E. Newman, “Modularity and community structure in networks”,  Proceedings of the national academy of sciences, 103(23), 8577-8582 (2006).
[9] S. Fortunato and M. Barthelemy, “Resolution limit in community detection”,  Proceedings of the national academy of sciences, 104(1), 36-41 (2007).
[10] A. Baudin, M. Danisch, S. Kirgizov, C. Magnien and M. Ghanem, “Clique percolation method: memory efficient almost exact communities”,  International Conference on Advanced Data Mining and Applications (pp. 113-127). Cham: Springer International Publishing (2022, January).
[11] W. Chen, Z. Liu, X. Sun and Y. Wang, “A game-theoretic framework to identify overlapping communities in social networks”,  Data Mining and Knowledge Discovery, 21, 224-240 (2010).
[12] L. Zhou, P. Yang, K. Lü, L. Wang and H. Chen, “A fast approach for detecting overlapping communities in social networks based on game theory”, British International Conference on Databases (pp. 62-73). Springer, Cham. (2015).
[13] G.L. Zacharias, J.E. Macmillan and S.B. Van Hemel, “Behavioral modeling and simulation: From individuals to societies”,  National Academies Press (2008).
[14] L. Zhou, C. Cheng, K. Lü and H. Chen, “Using coalitional games to detect communities in social networks”,   International Conference on Web-Age Information Management (pp. 326-331), Springer, Berlin, Heidelberg (2013).

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