A multi-user perspective for personalized email communities

Abstract

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 multiuser 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.

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

“…The clustering results should be combined with other detection methods to improve the detection accuracy. Waqas et al 13 proposed a graph clustering algorithm to solve the fraud detection problem in the email sending process. First, the email sending behavior of entities in the dataset was processed through graph clustering to obtain multi-user personalized communities.…”

Email Based Institutional Network Analysis: Applications and Risks

“…The work of Nawaz et al (2016) studied the grouping of individuals with similar neighbourhood and communication behaviour using email metadata, such as number of sent and received emails, subject length, text, email and attachment sizes, and the date and time. The community evolution process was studied using different clustering techniques, in addition to graph analyses.…”