| 摘要(英) |
In recent years, the rise of social websites, such as: Facebook, like the website has been widely used, these websites have a lot of information about the user. Many researchers have begun to study how to effectively analyze large amounts of social network data. Information diffusion and influence of social network analysis is one of the concerns being. However, recent studies usually only focus on the behavior of information diffusion between users, instead of considering the behavior of information diffusion between different social communities Therefore, this paper provides information diffusion between communities and influence of community in the social networks, find out the mutual influence between community relations. This research is composed of two parts: the first part, according to the community website provides Community information and messages between users spread and influence relationships define the degree of mutual influence between the two communities; the second part, the use of Randomized HITS algorithm methods to measure the influence rank of communities in social network. |
| 參考文獻 |
[1] Business Next
http://www.bnext.com.tw/
[2] insightxplorer
http://www.insightxplorer.com/index.html
[3] Digital Marketing Association
http://www.iama.org.tw/
[4] Buzz Lab
http://buzz.itrue.com.tw/blog_old/?cat=7
[5] Micheline Kamber, Jian Pei. “Data Mining: Concepts and Techniques, Second Edition, Elsevier Inc., San Francisco, 2006”.
[6] Matt Richardson, Pedro Domingos. “Mining Knowledge-Sharing Sites for Viral Marketing”, In Proceeding of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.
[7] Pedro Domingos, Matt Richardson. “Mining the network value of customers”, In Proceeding of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining.
[8] 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.
[9] 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.
[10] Bianchini, M., Gori, M., Scarselli, “F.: Inside PageRank”, ACM Transactions on Internet Technology 5(1), 92-128 (2006).
[11] 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.
[12] 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.
[13] Min-Feng Wang, Chi-Sheng Huang*, Meng-Feng Tsai, Bo-Ru Song,
Shin-Fu Su and Cheng-Hsien Tang. “Generalized Analysis of Message Propagation on Social Network” , International Journal of Future Generation Communication and Networking,Vol. 5, No. 2, June, 2012
[14] J. M. Kleinberg. “Authoritative source in a hyperlink environment”, Jour of the ACM(JACM), vol. 46, pp. 604-632, 1999.
[15] Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan. “Stable algorithms for link analysis”,In Proceeding SIGIR ’01 Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval Pages 258-266.
[16] Online social network research
http://socialnetworks.mpi-sws.org/
[17] SNAP
http://snap.stanford.edu/index.html
[18] eMarketer
http://www.emarketer.com |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit