Here is Dr. Cody Dunne speaking about a new information visualization technique called “Network Motif Simplification” at the recent CHI 2013 conference in Paris, France.

Dr. Cody Dunne at CHI 2013 (Photo Credit: Ben Shneiderman)

Networks, no matter how complex, are composed of simpler, smaller structures, called motifs.  Some of these structures are easy to identify, like the pattern of a “star” where a single node acts as the sole connection to a connected component for one or more “pendant” nodes with a single tie.  Another common pattern are nodes that are “parallel bridges” which share the only two connections they have with two or more other nodes.  These common structures  can be identified and removed and replaced with more efficient and comprehensible representations.

His paper with Prof. Ben Shneiderman at CHI 2013, “Motif simplification: improving network visualization readability with fan, connector, and clique glyphs“, demonstrates a novel method for improving the quality of network visualizations.  Common network motifs appear frequently in networks.  In network motif simplification these patterns are removed and replaced with simpler composite images:

Motifs collapse into simple glyphs

The result is a simplification of the network visualization, removing clutter to reveal the core structural properties of interest.

A complex network of voting relationships in the
2007 United State Senate is reduced to a simplified form

This method for collapsing complex network graphs into simpler forms has been implemented in NodeXL.  Look for the feature in the NodeXL Ribbon menu, in the NodeXL > Analysis > Groups > Group by Motif… option.

NodeXL implements network motif simplification

The feature allows users to select the types of motifs that should be recognized and collapsed:

Users select network motifs to find and replace

The paper has been reviewed by Stephen Few on the Perceptual Edge Visual Business Intelligence blog.

Here is Dr. Dunne’s video explaining and demonstrating the concept:

For more information about the project, see: http://www.cs.umd.edu/hcil/nicernetvis