Scalability considerations for multivariate graph visualization

Abstract

Scalability in visualization is a challenge: How do we choose to show more items than can be easily rendered upon a screen or understood by a human effectively? Multivariate graph visualization adds additional wrinkles in that nodes and edges are no longer atomic entities. Rather, they are repositories for further rich information. In information seeking, the mantra attributed to Ben Shneiderman succinctly outlines a path to visual scalability: "Overview first, zoom, then details-on-demand" [69].While this is good guidance, naively presenting the whole universe of data as an initial "overview" , leads to dense, unreadable displays (Fig. 10.1). To provide insightful visualizations at large scale for multivariate graphs, we must understand what our visual, cognitive, and architectural limits are, then explore approaches to mitigate these limitations. Detailed views must offer useful affordances for navigation to other views. The goals of this chapter are to identify the challenges and the state-of-the-art in these areas. © 2014 Springer International Publishing.