Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks,
they can be a valuable source of information. We present EmailTime, a visual analysis tool of email correspondence
patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence
in the email dataset. Our approach is to put time as a primary variable of interest, and plot emails along a time line.
EmailTime helps email dataset explorers interpret archived messages by providing zooming, panning, filtering and
highlighting etc. To support analysis, it also measures and visualizes histograms, graph centrality and frequency on the
communication graph that can be induced from the email collection. This paper describes EmailTime's capabilities,
along with a large case study with Enron email dataset to explore the behaviors of email users within different
organizational positions from January 2000 to December 2001. We defined email behavior as the email activity level of
people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These
metrics were calculated through EmailTime. Results showed specific patterns in the use email within different
organizational positions. We suggest that integrating both statistics and visualizations in order to display information
about the email datasets may simplify its evaluation.