  Email classification and prioritization expert systems have the potential to
automatically group emails and users as communities based on their
communication patterns, which is one of the most tedious tasks. The exchange of
emails among users along with the time and content information determine the
pattern of communication. The intelligent systems extract these patterns from
an email corpus of single or all users and are limited to statistical analysis.
However, the email information revealed in those methods is either constricted
or widespread, i.e. single or all users respectively, which limits the
usability of the resultant communities. In contrast to extreme views of the
email information, we relax the aforementioned restrictions by considering a
subset of all users as multi-user information in an incremental way to extend
the personalization concept. Accordingly, we propose a multi-user personalized
email community detection method to discover the groupings of email users based
on their structural and semantic intimacy. We construct a social graph using
multi-user personalized emails. Subsequently, the social graph is uniquely
leveraged with expedient attributes, such as semantics, to identify user
communities through collaborative similarity measure. The multi-user
personalized communities, which are evaluated through different quality
measures, enable the email systems to filter spam or malicious emails and
suggest contacts while composing emails. The experimental results over two
randomly selected users from email network, as constrained information, unveil
partial interaction among 80% email users with 14% search space reduction where
we notice 25% improvement in the clustering coefficient.
