  Event-based services have recently witnessed a rapid growth driving the way
people explore and share information of interest. They host a huge amount of
users' activities including explicit RSVP, shared photos, comments and social
connections. Exploiting these activities to detect communities of similar users
is a challenging problem. In reality, a community in event-based social network
(ESBN) is a group of users not only sharing common events and friends, but also
having similar topical interests. However, such community could not be detected
by most of existing methods which mainly draw on link analysis in the network.
To address this problem, there is a need to capitalize on the semantics of
shared objects along with the structural properties, and to generate
overlapping communities rather than disjoint ones. In this paper, we propose to
leverage the users' activities around events with the aim to detect communities
based on topical clustering and link analysis that maximize a new form of
semantic modularity. We particularly highlight the difference between online
and offline social interactions, and the influence of event categories to
detect communities. Experimental results on real datasets showed that our
approach was able to detect semantically meaningful communities compared with
existing state of the art methods.
