Honeycomb: Visual Analysis of Large Scale Social Networks

  • Authors:

  • Frank Ham;Hans-Jörg Schulz;Joan M. Dimicco

  • Affiliations:

  • IBM TJ Watson Research Center, Cambridge, USA 02142;University of Rostock, Rostock, Germany 18051;IBM TJ Watson Research Center, Cambridge, USA 02142

  • Venue:
  • INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
  • Year:
  • 2009

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Abstract

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 visualization 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 networking 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.