
Sanjay Kairam, Diana MacLean, Manolis Savva, Jeffrey Heer

AVI: The International Working Conference on Advanced Visual Interfaces, 2012


Visual methods for supporting the characterization, com parison, and classification of large networks remain an open challenge. Ideally, such techniques should surface useful structural features – such as effective diameter, smallworld properties, and structural holes – not always apparent from either summary statistics or typical network visualizations. In this paper, we present GraphPrism, a technique for visually summarizing arbitrarily large graphs through combinations of ‘facets’, each corresponding to a single node or edge specific metric (e.g., transitivity). We describe a generalized approach for constructing facets by calculating distributions of graph metrics over increasingly large local neighborhoods and representing these as a stacked multiscale histogram. Evaluation with paper prototypes shows that, with minimal training, static GraphPrism diagrams can aid network analysis experts in performing basic analysis tasks with network data. Finally, we contribute the design of an interactive system using linked selection between GraphPrism overviews and nodelink detail views. Using a case study of data from a coauthorship network, we illustrate how GraphPrism facilitates interactive exploration of network data.



This project is known for:
networks , analysis , graphs , visualization , scalability