Visual recommendations for network navigation

Tarik Crnovrsanin, Isaac Liao, Yingcai Wu, Kwan-Liu Ma

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1081-1090
Number of pages10
JournalComputer Graphics Forum
Issue number3
StatePublished - Jan 1 2011

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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