Social networks collected by historians or sociologists typically have a large number of attributes associated with their actors, and applying analysis algorithms to these networks will produce additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of graph attributes is one of the main challenges in multivariate social networks.
We present GraphDice, a multivariate network visualization system that uses the ScatterDice technique to support navigation in all possible graph attribute combinations.
A. Bezerianos, F. Chevalier, P. Dragicevic, N. Elmqvist and J.D. Fekete GraphDice: A System for Exploring Multivariate Social Networks In Proceedings of Eurographics/IEEE-VGTC Symposium on Visualization (Eurovis 2010), June 2010, Bordeaux, France
The paper has been presented at Eurovis2010, 9-11 June, 2010, Bordeaux, France.
Download the high quality video (avi, 46 MB).
|GraphDice functionalities||Example Scenarios|
Download the demo (executable jar) Two datasets are included in the archive: infovis.graphml and raweb.graphml