Discovery of User Communities from Web Audience Measurement Data

Authors:
Tsuyoshi Murata
Affiliations:
National Institute of Informatics (NII) and Japan Science and Technology Agency (JST)
Venue:
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2004

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Abstract

As the research of Web structure mining, several attempts have been made for discovering group of related Web pages (Web communities) such as Kumar's trawling and Flake's method. There are groups of users who watch such related Web pages, and discovering such groups (user communities) is important for clarifying the behaviors of the users of similar tastes. Moreover, it is expected that the characteristics of user communities in the Web correspond to that in real human societies. A method for discovering user communities is described in this paper. Client-level log data (Web audience measurement data) is used as the data of users' Web watching behaviors. Maximal complete bipartite graphs are searched from the graph obtained from the log data without analyzing the contents of Web pages. Experimental results show that our method succeeds in discovering many interesting user communities with labels that characterize the communities.