Social network clustering and visualization using hierarchical edge bundles

Yuntao Jia, Michael Garland, John C. Hart

Research output: Contribution to journalArticlepeer-review


The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight-line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community-based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real-world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on and let users explore the visualization and community clustering of their own social networks.

Original languageEnglish (US)
Pages (from-to)2314-2327
Number of pages14
JournalComputer Graphics Forum
Issue number8
StatePublished - Dec 2011


  • Betweenness centrality
  • Edge bundles
  • Network clustering
  • Visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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