Honeycomb: Visual Analysis of Large Scale Social Networks

  • Frank van Ham
  • Hans-Jörg Schulz
  • Joan M. Dimicco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5727)


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.


Social Network Adjacency Matrix Social Network Analysis Social Network Site Social Network Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Frank van Ham
    • 1
  • Hans-Jörg Schulz
    • 2
  • Joan M. Dimicco
    • 1
  1. 1.IBM TJ Watson Research CenterCambridgeUSA
  2. 2.University of RostockRostockGermany

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