Banzhaf Index for Influence Maximization
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.
KeywordsSocial Network Centrality Measure Collaboration Network Marginal Contribution Coalition Game
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- 2.Banzhaf, J.F.: Weighted voting doesn’t work: a mathematical analysis. Rutgers Law Review 19, 317–343 (1965)Google Scholar
- 3.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)Google Scholar
- 4.Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. KDD (2009)Google Scholar
- 5.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)Google Scholar
- 7.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)Google Scholar
- 10.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)Google Scholar
- 11.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)Google Scholar
- 13.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)Google Scholar
- 14.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)Google Scholar