  Email service providers have employed many email classification and
prioritization systems over the last decade to improve their services. In order
to assist email services, we propose a personalized email community detection
method to discover the groupings of email users based on their structural and
semantic intimacy. We extract the personalized social graph from a set of
emails by uniquely leveraging each node with communication behavior.
Subsequently, collaborative similarity measure (CSM) based intra-graph
clustering approach detects personalized communities. The empirical analysis
shows effectiveness of the resultant communities in terms of evaluation
measures, i.e. density, entropy and f-measure. Moreover, email strainer,
dynamic group prediction, and fraudulent account detection are suggested as the
potential applications from both the service provider and user's point of view.
