Visual recommendations for network navigation

Authors:
Tarik Crnovrsanin;Isaac Liao;Yingcai Wuy;Kwan-Liu Ma
Affiliations:
The Visualization and Interface Design Innovation Research Group, University of California, Davis;The Visualization and Interface Design Innovation Research Group, University of California, Davis;The Visualization and Interface Design Innovation Research Group, University of California, Davis;The Visualization and Interface Design Innovation Research Group, University of California, Davis
Venue:
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Year:
2011

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Abstract

Understanding large, complex networks is important for many critical tasks, including decision making, process optimization, and threat detection. Existing network analysis tools often lack intuitive interfaces to support the exploration of large scale data. We present a visual recommendation system to help guide users during navigation of network data. Collaborative filtering, similarity metrics, and relative importance are used to generate recommendations of potentially significant nodes for users to explore. In addition, graph layout and node visibility are adjusted in real-time to accommodate recommendation display and to reduce visual clutter. Case studies are presented to show how our design can improve network exploration.