COMMUNITY DETECTION IN THE COLLABORATIVE WEB

International Journal of Managing Information Technology (IJMIT)
ISSN: 0975-5586 (Online); 0975-5926 (Print)


Article:
COMMUNITY DETECTION IN THE COLLABORATIVE WEB

Authors:
Lylia Abrouk, David Gross-Amblard and Nadine Cullot 
LE2I, UMR CNRS 5158 
University of Burgundy, Dijon, France 

Abstract:
Most of the existing social network systems require from their users an explicit statement of their friendship relations. In this paper we focus on implicit Web  communities and present an approach to automatically detect them, based on user’s resource manipulations. This approach is dynamic as user groups appear and evolve along with users interests over time. Moreover, new resources are dynamically labelled according to who is manipulating them. Our proposal relies on the fuzzy K-means clustering method and is assessed on large movie datasets. 


KEYWORDS
Clustering, Data sharing, Information networks, user distance, Web community.

Original Source URL: http://airccse.org/journal/ijmit/papers/1110ijmit01.pdf

http://airccse.org/journal/ijmit/vol2.html




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