  The increasing importance of Web 2.0 applications during the last years has
created significant interest in tools for analyzing and describing collective
user activities and emerging phenomena within the Web. Network structures have
been widely employed in this context for modeling users, web resources and
relations between them. However, the amount of data produced by modern web
systems results in networks that are of unprecedented size and complexity, and
are thus hard to interpret. To this end, community detection methods attempt to
uncover natural groupings of web objects by analyzing the topology of their
containing network. There are numerous techniques adopting a global perspective
to the community detection problem, i.e. they operate on the complete network
structure, thus being computationally expensive and hard to apply in a
streaming manner. In order to add a local perspective to the study of the
problem, we present Bridge Bounding, a local methodology for community
detection, which explores the local network topology around a seed node in
order to identify edges that act as boundaries to the local community. The
proposed method can be integrated in an efficient global community detection
scheme that compares favorably to the state of the art. As a case study, we
apply the method to explore the topic structure of the LYCOS iQ collaborative
question/answering application by detecting communities in the networks created
from the collective tagging activity of users.
