Communications of the ACM
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Web-Based Reputation Management Systems: Problems and Suggested Solutions
Electronic Commerce Research
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Computational & Mathematical Organization Theory
Proceedings of the 2006 international workshop on Mining software repositories
Methods for comparing rankings of search engine results
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
Linked: How Everything Is Connected to Everything Else and What It Means
Linked: How Everything Is Connected to Everything Else and What It Means
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
Comparing rankings of search results on the Web
Information Processing and Management: an International Journal - Special issue: Infometrics
Generalized distances between rankings
Proceedings of the 19th international conference on World wide web
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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.