Large-Scale Graph Visualization and Analytics by Kwan-Liu Ma and Chris W. Muelder. (Computer, June 2013)
Abstract:
Novel approaches to network visualization and analytics use sophisticated metrics that enable rich interactive network views and node grouping and filtering. A survey of graph layout and simplification methods reveals considerable progress in these new directions.
We have all seen large graph hairballs that are as impressive as they are informative. Impressive to someone who has recently discovered “…everything is a graph…” but not to anyone else.
Ma and Muelder do an excellent job of constrasting traditional visualizations that result in “…an unintelligible hairball—a tangled mess of lines” versus more informative techniques.
Among the methods touched upon are:
- treemap
- space-filling curve (SFC)
- SFC Gosper
- SFC Hilbert (aka Hilbert R-Tree)
- Semantic Substrates
- Centrality sensitivity
References at the end of the article should get you started towards useful visualizations of large scale graphs.
PS: I assume the article is based in part on C.W. Muelder’s “Advanced Visualization Techniques for Abstract Graphs and Computer Networks,” PhD dissertation, Dept. Computer Science, University of Calif., Davis, 2011. It is cited among the references. Published by ProQuest which means the 130 page dissertation runs $62.10 in paperback.
Let me know if you run across a more reasonably accessible copy.
I first saw this in a tweet by Paul Blaser.