The Web is a huge network composed of Web pages and hyperlinks. It
is often reported that related Web pages are densely linked
with each other. Finding groups of such related pages,
which are called Web communities, is important for information
retrieval from the Web. Several attempts have been made for the
discovery of Web communities such as Kumar's trawling and Flake's
method.
In addition to the communities of related Web pages, there are
communities of users sharing common interests. Finding the latter
communities, which we called user communities in this paper,
is also important for clarifying the behaviors of Web users.
It is expected that the characteristics of user communities
in the Web correspond to those in real human communities.
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 term-user 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.
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