Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
Graph-based ranking algorithms for e-mail expertise analysis
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Beyond "from" and "received": exploring the dynamics of email triage
CHI '05 Extended Abstracts on Human Factors in Computing Systems
When can i expect an email response? a study of rhythms in email usage
ECSCW'03 Proceedings of the eighth conference on European Conference on Computer Supported Cooperative Work
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Blog cascade affinity: analysis and prediction
Proceedings of the 18th ACM conference on Information and knowledge management
Hermes: clustering users in large-scale e-mail services
Proceedings of the 1st ACM symposium on Cloud computing
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaboration recommendation on academic social networks
ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
Information spreading in context
Proceedings of the 20th international conference on World wide web
Understanding Crowds' Migration on the Web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Foundations for the design of visualizations that support trust in distributed teams
Proceedings of the International Working Conference on Advanced Visual Interfaces
Mining social relationship types in an organization using communication patterns
Proceedings of the 2013 conference on Computer supported cooperative work
Analyzing Communication Interaction Networks (CINs) in enterprises and inferring hierarchies
Computer Networks: The International Journal of Computer and Telecommunications Networking
Event detection using user interaction behavior models
Artificial Intelligence Review
Indirect weighted association rules mining for academic network collaboration recommendations
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Affinity-driven blog cascade analysis and prediction
Data Mining and Knowledge Discovery
Effective email network visualization techniques by means of user behaviors
Intelligent Data Analysis
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We examine the behavioral patterns of email usage in a large-scale enterprise over a three-month period. In particular, we focus on two main questions: (Q1) what do replies depend on? and (Q2) what is the gain of augmenting contacts through the friends of friends from the email social graph? For Q1, we identify and evaluate the significance of several factors that affect the reply probability and the email response time. We find that all factors of our considered set are significant, provide their relative ordering, and identify the recipient list size, and the intensity of email communication between the correspondents as the dominant factors. We highlight various novel threshold behaviors and provide support for existing hypotheses such as that of the least-effort reply. For Q2, we find that the number of new contacts extracted from the friends-of-friends relationships amounts to a large number, but which is still a limited portion of the total enterprise size. We believe that our results provide significant insights towards informed design of advanced email features, including those of social-networking type.