2012 Poster Sessions : GraphPrism: Compact Visualization of Complex Network Structure

Student Name : Sanjay Kairam
Advisor : None
Research Areas: Artificial Intelligence
Abstract:
Visualization has remained an important means for understanding the structural aspects of networks, social and otherwise. The design of techniques for supporting the visual analysis of large, complex networks, however, remains an open challenge.

In this poster, we present GraphPrism, a technique for visually summarizing arbitrarily large networks for the purposes of characterizing, comparing, and classifying such data. By abstracting away some details about individual nodes and edges, GraphPrism diagrams allow viewers to quickly ascertain important aspects of network structure, such as the effective diameter, small-world properties, and the presence of structural holes. This poster will describe how these diagrams are constructed and present results from an evaluation conducted with network analysis experts; these results show that even static paper prototypes of GraphPrism diagrams can aid in basic network analysis tasks after only minimal training.

Bio:
Sanjay is a second-year Ph.D. student advised by Jeffrey Heer in the Computer Science Department at Stanford University. His research focuses on modeling and visualizing human behavior in large social and information networks and communities.

Prior to starting graduate school, Sanjay worked with Peter Pirolli and the Augmented Social Cognition group at the Palo Alto Research Center on several projects pertaining to social search and information-seeking. Sanjay received his B.S. in Mathematics and M.A. in Philosophy in 2006, also from Stanford, along with a minor in the interdisciplinary Symbolic Systems program.