Abstract
Social media has changed the way people communicate with each other and has brought people together. Enterprises are increasingly using it as a medium for marketing activities. However, due to the size of these networks, marketers often look for key customers (influencers) to drive the campaign to the community. In this paper, we take a game theoretic approach to identify key influencers in a network. We begin with defining coalition games to model the social network and then use the concept of Banzhaf index to measure the utility of each user to the coalition. We further extend this concept towards identification of influencers and compare the resulting algorithm against existing works on influence maximization on several datasets. Improvements are observed.
Keywords
- Social Network
- Centrality Measure
- Collaboration Network
- Marginal Contribution
- Coalition Game
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your 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
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)
Banzhaf, J.F.: Weighted voting doesn’t work: a mathematical analysis. Rutgers Law Review 19, 317–343 (1965)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1029–1038. ACM (2010)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. KDD (2009)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 57–66. ACM (2001)
Everett, M.G., Borgatti, S.: The centrality of groups and classes. Journal of Mathematical Sociology 23(3), 181–201 (1999)
Fatima, S.S., Wooldridge, M., Jennings, N.: A randomized method for the shapley value for the voting game. In: Proceedings of the 11th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 955–92. AAMAS (2007)
Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443 (1978)
Grofman, B., Owen, G.: A game theoretic approach to measuring centrality in social networks. Social Networks 4, 213–224 (1982)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp, 137–146. ACM (2003)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 420–429. ACM (2007)
Matsui, Y., Matsui, T.: Np-completeness for calculating power indices of weighted majority games. Theoretical Computer Science 263(1–2), 305–310 (2001)
Narayanam, R., Narahari, Y.: A shapley value-based approach to discover influential nodes in social networks. IEEE Transactions on Automation Science and Engineering 99, 1–18 (2010)
Young, H.P.: The diffusion of innovations in social networks. In: Proceedings volume in the Santa Fe Institute studies in the sciences of complexity Santa Fe Institute Studies on the Sciences of Complexity, vol. 3, pp. 267–282. Oxford University Press, US (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Srinivasan, B.V., Kumar, A.S. (2015). Banzhaf Index for Influence Maximization. In: Liu, TY., Scollon, C., Zhu, W. (eds) Social Informatics. SocInfo 2015. Lecture Notes in Computer Science(), vol 9471. Springer, Cham. https://doi.org/10.1007/978-3-319-27433-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-27433-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27432-4
Online ISBN: 978-3-319-27433-1
eBook Packages: Computer ScienceComputer Science (R0)