Applying Social Networks Analysis Methods to Discover Key Users in an Interest-Oriented Virtual Community
In recent years, with the growth of Internet technology and virtual community, the users in virtual community not only play as the information receiver but also very important role to provide information. However, information overload has becoming a very serious problem and how to find information efficiently is also an important issue. In this research, we believe that users in a virtual community may affect each other, especially those with high influence. Therefore, we propose an architecture to discover the key users in a virtual community. By applying the architecture, it would be a very efficient and low cost approach. In the architecture, social networks analysis and visualization technique will be the main methods to discover the key users. In this paper, we also present an experiment to demonstrate the proposed method and the analysis results.
KeywordsSocial Network Analysis Cluster Coefficient Online Social Networking Small World Small World Network
Unable to display preview. Download preview PDF.
- 1.Adamic, L.A., Adar, E.: Friends and neighbors on the web. Social Networks 25, 211–230 (2003) Google Scholar
- 2.Armstrong, A.G., Hagel III, J.: Net Gain: Expanding Markets Through Virtual Communities. Harvard Business School Press, MA (1997) Google Scholar
- 3.Scott, J.: Social Network Analysis: A Hand Book. SAGE Publication (2002) Google Scholar
- 4.Rheingold, H.: The Virtual Community: Homesteading on the Elec-tronic Frontier. Addison-Wesley, MA (1993) Google Scholar
- 5.Adler, R.P., Christopher, A.J.: Internet Community Primer Overview and Business Opportunity, http://www.digiplaces.com Google Scholar
- 6.Ahmed, A., Batagelj, V., Fu, X., Hong, S.-H., Merrick, D., Mrvar, A.: Visualisation and Analysis of the Internet Movie Database. In: Asia-Pacific Symposium on Visualisation, pp. 17–24 (2007) Google Scholar
- 7.Jakob, N., Weber, S.H., Muller, M.C., Gurevych, I.: Beyond the Stars: Exploiting Free-Text User Reviews to Improve the Accuracy of Movie Recommendations. In: TSA 2009, Hong Kong, China (2009) Google Scholar
- 8.Debnath, S., Ganguly, N., Mitra, N.: Feature Weighting in Content Based Recommendation System Using Social Network Analysis. In: Proceedings of the 17th International Conference on World Wide Web (2008) Google Scholar
- 9.Scott, J.: Social Network Analysis: Critical Concepts in Sociology. Routledge, New York (2002) Google Scholar
- 10.Milgram, S.: The Small-World Problem. Psychology Today, 61–67 (1967) Google Scholar
- 11.Watts, D.J.: Collective Dynamics of “Small World” Networks. Na-ture 393, 440–442 (1998) Google Scholar
- 12.Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., Christakis, N.: Tastes, Ties, and Time: A New Social Network Dataset Using Face-book.com. Social Networks (2008) Google Scholar