Measuring the Importance of Users in a Social Network Based on Email Communication Patterns

Email communication patterns have been long used to derive the underlying social network structure. By looking at who is talking to who and how often, the researchers have disclosed interesting patterns, hinting on social roles and importance of actors in such networks. Email communication analysis has been previously applied to discovering cliques and fraudulent activities (e. g. the Enron email network), to observe information dissemination patterns, and to identify key players in email communication networks. In this paper we are using a large dataset of email communication within a constrained community (i.e. the employees of a single institution) to discover the importance of employees in the underlying network. Contrary to previous attempts, though, we are scrutinizing the delays in answering emails. We base our method on a simple notion of implicit importance: people are more likely to quickly respond to emails sent by people who are being perceived as important. In the paper we propose several methods for building the social network from the email communication data and we introduce various weighting schemes. We aggregate the resulting rankings and compute differences between rankings to observe the stability of our method. We also compare the resulting rankings with an a priori assessment of employees' importance to verify our method. The results of the conducted experiments clearly show the validity and robustness of the proposed method.