Abstract
In this paper the utility of using the Self Organizing Maps (SOM), in conjunction with U-matrix, to visualize the evolution of a social network community formed by a set of blogs is shown. Weblogs are dynamic websites updated via easy-to-use content management systems whose links tend to mirror or in some cases establish new types of social relations, thereby creating a social network. Analyzing the evolution of this network allows the discovery of emerging social structures and their trends in growth. Here we apply this method to Blogalia, a blog hosting site from which we have a complete set of data. The proposed procedure not only gives some insight on how communities form and evolve, but would also enable to predict the future paths that their members will take.
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
Wikipedia, the free encyclopedia, http://en.wikipedia.org/wiki/WebLog
The Blogging Iceberg: http://www.perseus.com/blogsurvey/thebloggingiceberg.html
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Series in Information Sciences. Springer, New York (2001)
Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: SOM_PAK: The Self-Organizing Map Program Package. Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, FIN-02150 Espoo, Finland (1996)
SOM Toolbox is Copyright (C), by Esa Alhoniemi, Johan Himberg, Juha Parhankangas and Juha Vesanto employed in the Laboratory of Information and Computer Science in the Helsinki Univ. of Technology (2000), http://www.cis.hut.fi/projects/somtoolbox/
Ultsch, A.: Self-organizing neural networks for visualization and classification. In: Opitz, O., Lausen, B., Klar, R. (eds.) Information and Classification, pp. 307–313. Springer, London (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prieto, B., Merelo, J.J., Prieto, A., Tricas, F. (2007). Analyzing a Web-Based Social Network Using Kohonen’s SOM. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_110
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)