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10.5120/21810-5133 |
Smita Bhosale and Dhanshree Kulkarni. Article: Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm. International Journal of Computer Applications 122(19):28-31, July 2015. Full text available. BibTeX
@article{key:article, author = {Smita Bhosale and Dhanshree Kulkarni}, title = {Article: Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm}, journal = {International Journal of Computer Applications}, year = {2015}, volume = {122}, number = {19}, pages = {28-31}, month = {July}, note = {Full text available} }
Abstract
A mobile social network plays an important role as the spread of information and influence in the form of "word-of-mouth". It is basic thing to find small set of influential people in a mobile social network such that targeting them initially. It will increase the spread of the influence . The problem of finding the most influential nodes in network is NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation. Community based Greedy algorithm is used for mining top-K influential nodes. It has two components: dividing the mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Location Based community Greedy algorithm is used to find the influence node based on Location and consider the influence propagation within Particular area. Experiments result on real large-scale mobile social networks show that the proposed location based greedy algorithm has higher efficiency than previous community greedy algorithm.
References
- W. Yu, G. Cong, G. Song, and K. Xie, "Community-based greedy algo- rithm for mining top-k in?uential nodes in mobile social networks," in KDD, 2010, pp. 1039–1048
- F. Bass, "A new product growth model for consumer durables," Manage- ment Science, vol. 15, pp. 215–227, 1969.
- V. Mahajan, E. Muller, and F. Bass, "New product diffusion models in marketing: A review and directions for research," Journal of Marketing, vol. 54, no. 1, pp. 1–26, 1999.
- D. Kempe, J. Kleinberg, and E. Tardos, "Maximizing the spread of in?uence through a social network," in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 137–146.
- J. Brown and P. Reinegen, "Social ties and word-of-mouth referral be- havior," Journal of Consumer research, vol. 14, no. 3, pp. 350–362, 1987.
- W. Chen, Y. Wang, and S. Yang, "Ef?cient in?uence maximization in social networks," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 199– 208.
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance, "Cost-effective outbreak detection in networks," in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, pp. 420–429.
- M. Girvan and M. E. J. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, vol. 99, no. 12, pp. 7821–7826, 2002.
- P. Domingos and M. Richardson, "Mining the network value of cus- tomers," in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001, pp. 57–66.
- D. Kempe, J. Kleinberg, and E. Tardos, "In?uential nodes in a diffusion model for social networks," In ternational colloquium on automata, languages and programming, no. 32, pp. 112–1138, 2005
- J. Goldenberg, B. Libai, and E. Muller, "Talk of the network: A complex systems look at the underlying process of word-of-mouth," Marketing Letters, vol. 12, no. 3, pp. 211–223, 2001