摘要(英) |
The structure and scale of the Internet is tremendous. It’s not easy to do research in the domain of data analysis and data mining. With the rise of the social network, there are more and more research showing how to make use the structure of social network, and to find the most influence nodes to maximize the influence spread. This research is composed of two parts: In the first part, we will cluster the network by SetCover and Label algorithm, and apply the modularity function to determine the length of path. In the second part, we will propose two different methods to measure the influence rank of nodes in social network. For the first method, we consider about that the influence of node for their community and for all communities simultaneously. Different from first method, the second method select the most influence nodes for their communities. Next, we select the most influence node for the other communities as well. By proposing the selection of the influence nodes in the structure of social network, the behavior of social network could be analyzed by experts. It also can support web marketing for enterprises to spend less cost to reach maximum benefits.
|
參考文獻 |
[1] V. Mahajan, E. Muller, F. Bass. New Product Diffusion Models in Marketing: A Review and Directions for Research. Journal of Marketing 54:1(1990) pp. 1-26
[2] J Goldenberg, B. Libai, E. Muller. Talk of the Network: A Complex Sustem Look at the Underlying Process of Word-of-Mouth. Marketing Letters 12:3(2001), 211-223.
[3] P. Domingos and M. Richardson, “Mining the network value of customers,” in KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM Press, 2001, pp.57-66
[4] D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In the Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 137-146,2003.
[5] Tian Zhu, Bin Wu, Bai Wang.Social Influence and Role Analysis Based on Community Structure in Social Network. Proc.
Advanced Data Mining and Applications,2009,pp.788-795.
[6] Brai,S., Page,L.: The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the 7th World Wide Web Conference (WWW7), Brisbane, Australia (1998)
[7] Bianchini, M., Gori, M., Scarselli, F.: Inside PageRank. ACM Transactions on Internet Technology 5(1), 92-128 (2006)
[8] Kimura, M.; Saito, K.; Nakano, R.; and Motoda, H. 2010.
Extracting influential nodes on a social network for information diffusion. Data Mining and Knowledge Discovery
20:70–97.
[9] R. Narayanam and Y. Narahari, “A shapley value based
approach to discover influential nodes in social networks,”
IEEE Transactions on Automation Science and Engineering 99,
1-18,2010.
[10] W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In KDD, pages 199-208, 2009.
[11] J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBrisesn, and N. S. Glance. Cost-effective outbreak detection in networks. In Proc. SIGKDD, pages 420-429, 2007.
[12] Leicht, E. A. and M. E. J. Newman. 2008. Community structure in directed networks. Physical Review Letters 100:
XX. American Express, 2011. Sources New Customers Use to Find Them according to US Small Business. Graph. Available from:
http://searchenginewatch.com/3642207
[13] P. A. Estevez, P. A. Vera, and K. Saito. Selecting the most influential nodes in social networks. In Proceedings of the International Joint Conference on Neural Networks, pages 2397–2402, 2007
[14] Y. Wang, G. Cong, G. Song, and K. Xie. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2010
[15] U. N. Raghavan, R. Albert, and S. Kumara. Near linear time algorithm to detect community structures in large-scale networks. In Phys. Rev. E76, 2007.
[16] PewInternet
http://libraryview.wordpress.com/2011/02/25/1029/
[17] emarket
http://tobydawsonmarketing.blogspot.com/2011/04/consumer-control-interactivity-and-word.html
[18] Buzfactor
http://www.buzfactor.com/tag/online-marketing/
[19] Social Commerce Today:
http://socialcommercetoday.com/word-of-mouth-still-most-trusted-resource-says-nielsen-implications-for-social-commerce/
[20] SNAP
http://snap.stanford.edu/snap/download.html
|