Abstract
In social network, influences are propagating among users. Influence maximizing is an important problem which has been studied widely in recent years. However, in the real world, sometimes users need to make their influence maximization through social network and defeat their competitors under minimum cost, such as online voting, expanding market share, etc. In this paper, we consider the problem of selecting a seed set with the minimum cost to influence more people than other competitors. We show this problem is NP-hard and propose a cost-effective greedy algorithm to approximately solve the problem and improve the efficiency based on the submodularity. Furthermore, a new cost-effective Degree Adjust heuristics is proposed to get high efficiency. Experimental results show that our cost-effective greedy algorithm achieves better effectiveness than other algorithms and the cost-effective Degree Adjust heuristic algorithm achieves high efficiency and gets better effectiveness than Degree and Random heuristics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)
Wei, C., Yajun, W., Siyu, Y.: Efficient influence maximization in social networks. In: SIGKDD, pp. 199–208 (2009)
Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 306–311. Springer, Heidelberg (2007)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)
Jung, K., Heo, W., Chen, W.: Irie: Scalable and robust influence maximization in social networks. In: ICDM, pp. 918–923 (2012)
He, X., Song, G., Chen, W., et al.: Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model. In: SDM (2012)
Dubey, P., et al.: Competing for Customers in a Social Network. Social Science Electronic Publishing (2006)
Goyal, A., et al.: On minimizing budget and time in influence propagation over social networks. In: Social Network Analysis and Mining, pp. 179–192 (2013)
Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 539–550. Springer, Heidelberg (2010)
Budak, C., Agrawal, D., Abbadi, A.E.: Limiting the spread of misinformation in social networks. In: WWW, pp. 665–674 (2011)
Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincn, D., Sun, X., Wang, Y., Wei, W., Yuan, Y.: Influence maximization in social networks when negative opinions may emerge and propagate. In: SDM, pp. 379–390 (2011)
Zhang, P., Chen, W., Sun, X., et al.: Minimizing seed set selection with probabilistic coverage guarantee in a social network. In: KDD, pp. 1306–1315 (2014)
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
Liu, Z., Hong, X., Peng, Z., Chen, Z., Wang, W., Song, T. (2015). Minimizing the Cost to Win Competition in Social Network. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-25255-1_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25254-4
Online ISBN: 978-3-319-25255-1
eBook Packages: Computer ScienceComputer Science (R0)