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.

GraphDice is a multivariate network visualization system that uses the ScatterDice technique to support navigation in all possible graph attribute combinations.