Title
Analyzing Communication Interaction Networks (CINs) in enterprises and inferring hierarchies
Abstract
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].
Year
DOI
Venue
2013
10.1016/j.comnet.2012.11.028
Computer Networks
Keywords
DocType
Volume
Communication Interaction Network,Topology characterization,Hierarchy identification
Journal
57
Issue
ISSN
Citations 
10
1389-1286
5
PageRank 
References 
Authors
0.41
13
4
Name
Order
Citations
PageRank
Yi Wang11520135.81
Marios Iliofotou247618.49
Michalis Faloutsos35288586.88
Bin Wu429052.43