Although the discovery and analysis of communication patterns in large complex email datasets is a difficult task, it can be a valuable source of information. We describe the design and visualization technique of EmailTime, a tool for visual analysis of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks. EmailTime helps email dataset explorers interpret archived messages by providing interactions, visualizing histograms and measuring centrality (To, Cc and Sent) and frequency (sent and received). We performed case studies on the Enron dataset to discover impacts of executive position on the email behavior of organizational workers using a series of metrics e.g. number of sent and received emails as determined by From:, To: and Cc: fields, recipient counts of sent emails. In addition, we evaluated the visualization through pilot and user studies to find out whether users were able to recognize the selected capabilities.
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