Classifying organizational roles using email social networks

Abstract

This paper addresses the problem of role classification, which is related to classifying and grouping email users into a collection of organizational roles. This classification can be used in designing modern email clients by adding an Inbox prioritizing feature that can predict the role of a sender to the recipient of an email. A comprehensive study has been done on the social network of the Enron dataset. For classifying organizational roles, a feature vector containing a set of social network metrics and interaction-based features reflecting users' engagingness and responsiveness in their community is created. After representing each role in this feature space, Expectation Maximization (EM) algorithm has been applied to evaluate the extracted feature set. In turn, a Neural Network classifier has been built based on the extracted features for classifying organizational roles that resulted in 63.57% of accuracy. © 2013 Springer-Verlag.