Proceedings of the 12th IFIP TC13 Conference in Human-Computer Interaction (2009) pp. 429-442 Published by Springer
The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visuali- zation tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social net- working site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.
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