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Multivariate Networks: A Novel Edge Visualization Approach for Graph-based Visual Analysis Tasks

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Published:07 May 2016Publication History

ABSTRACT

Providing insight in complex networks or graphs with multivariate data is one of the main challenges for visual analysis today. Much work has been done for visualizing information on nodes, but the space in between has mostly not been used yet. We present the current progress of our approach for using this free space to visualize additional information. We developed two techniques called Partially filled Bars and Bars of Varying Height. Both techniques enable presenting multiple attribute values on the edges of a network simultaneously. We briefly discuss first use cases for such interfaces as well as advantages and disadvantages of both techniques. For proof the concept, a preliminary evaluation has been performed. The results show, that both techniques are promising for many use cases.

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            cover image ACM Conferences
            CHI EA '16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems
            May 2016
            3954 pages
            ISBN:9781450340823
            DOI:10.1145/2851581

            Copyright © 2016 Owner/Author

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 7 May 2016

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            CHI EA '16 Paper Acceptance Rate1,000of5,000submissions,20%Overall Acceptance Rate6,164of23,696submissions,26%

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