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Social network analysis is a powerful tool used to help analysts discover relationships amongst groups of people as well as individuals. It is the mathematics behind such social networks as Facebook and MySpace. These networks alone cause a huge amount of data to be generated and the issue is only compounded once one adds in other electronic media such as e-mails and twitter. In this paper we outline the basics of social network analysis and how it may be used in current and future Air Force applications.

We propose a set of common sense steps required to develop a recommender system for visual analytics. Such a system is an essential way to get additional mileage out of costly user studies, which are typically archived post publication. Crucially, we propose conducting user studies in a manner that allows machine learning techniques to elucidate relationships between experimental data (i.e., user performance) and metrics about the data being visualized and candidate visual representations. We execute a case study within our framework to extract simple rules of thumb that relate different data metrics and visualization characteristics to patterns of user errors on several network analysis tasks. Our case study suggests a research agenda supporting the development of general, robust visualization recommender systems.

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