On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
On maximum clique problems in very large graphs
External memory algorithms
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Scalable discovery of hidden emails from large folders
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Automated social hierarchy detection through email network analysis
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Behavioral profiles for advanced email features
Proceedings of the 18th international conference on World wide web
Inferring the Maximum Likelihood Hierarchy in Social Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Finding hierarchy in directed online social networks
Proceedings of the 20th international conference on World wide web
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With the proliferation of electronic modes of communication (e.g., e-mails, short messages), employees inside an enterprise can form several distinct Communication Interaction Networks, or CINs for short. A CIN is essentially a graph representation of ''who talks to whom'' among a group of individuals. In this paper, we conduct an empirical study of two modern enterprises and focus on three main questions: (Q1) How CINs from the two enterprises look; (Q2) How employees use the different available communication modes within an enterprise; and (Q3) By only using CINs, how much information we can extract regarding the hierarchy in the enterprise. We address these questions using empirical CINs from the Enron Corporation and a communication provider, using information from the exchange of e-mails, phone-calls, and short messages (SMS). For Q1, we reveal the following key structural properties that are shared by all the CINs in our study: they have high edge density, high clustering coefficient, and close to zero assortativity coefficient. For Q2, we observe that employees have differences in how they use the various communication modes. This suggests that different CINs capture different behavioral properties within an enterprise. For Q3, we propose HumanRank, a method of ranking individuals based on their importance (e.g., CEOs having higher rank than ordinary employees) using only the interactions between them. Next, using HumanRank, we introduce an unsupervised and parameter-free algorithm that identifies hierarchies by separating managers from ordinary employees. Our algorithm achieves above 70% accuracy and outperforms the state-of-the-art [23].