Abstract
The community detection in social networks is important to understand the structural and functional properties of networks. In this paper we propose a coalitional game model for community detection in social networks, and use the Shapley Value in coalitional games to evaluate each individual’s contribution to the closeness of connection. We then develop an iterative formula for computing the Shapley Value to improve the computation efficiency. We further propose a hierarchical clustering algorithm GAMEHC to detect communities in social networks. The effectiveness of our methods is verified by preliminary experimental result.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ahn, Y.Y., et al.: Link communities reveal multi-scale complexity in networks. Nature 466, 761–764 (2010)
Fatima, S.S., et al.: A linear approximation method for the Shapley Value. Artificial Intelligence 172, 1673–1699 (2008)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Aadithya, K.V., Ravindran, B., Michalak, T.P., Jennings, N.R.: Efficient Computation of the Shapley Value for Centrality in Networks. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 1–13. Springer, Heidelberg (2010)
Lemke, C., Howson, J.: Equilibrium points of bimatrix games. Journal of the Society of Industrial and Applied Mathematics, 413–424 (1964)
Lin, Y.R., et al.: Community Discovery via Metagraph Factorization. ACM Transactions on Knowledge Discovery from Data 5(3), 17 (2011)
Liu, W.Y., et al.: An approach for multi-objective categorization based on the game theory and Markov process. Applied Soft Computing 11, 4087–4096 (2011)
Liu, W.Y., et al.: Intelligent Data Analysis. Science Press, Beijing (2007)
Moretti, S., et al.: Using coalitional games on biological networks to measure centrality and power of genes. Bioinformatics 26(21), 2721–2730 (2010)
Owen, G.: Multilinear extensions of games. Management Science 18(5), 64–79 (1972)
Saad, W., et al.: Coalitional game theory for communication networks: a tutorial. IEEE Signal Processing Magazine 26(5), 77–97 (2009)
Shapley, L.S.: A Value for N-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to be Theory of Games, pp. 307–317. Princeton University Press (1953)
Shi, C., et al.: Multi-objective community detection in complex networks. Applied Soft Computing 12, 850–859 (2012)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33(4), 452–473 (1977)
Zhao, Z.Y., et al.: Topic oriented community detection through social objects and link analysis in social networks. Knowledge-Based Systems 26, 164–173 (2012)
Zlotkin, G., Rosenschein, J.: Coalition cryptography and stability mechanisms for coalition formation in task oriented domains. In: Association for the Advancement of Artificial Intelligence, pp. 432–437 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, L., Cheng, C., Lü, K., Chen, H. (2013). Using Coalitional Games to Detect Communities in Social Networks. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-38562-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38561-2
Online ISBN: 978-3-642-38562-9
eBook Packages: Computer ScienceComputer Science (R0)