2010
DOI: 10.1109/tvcg.2010.205
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The FlowVizMenu and Parallel Scatterplot Matrix: Hybrid Multidimensional Visualizations for Network Exploration

Abstract: A standard approach for visualizing multivariate networks is to use one or more multidimensional views (for example, scatterplots) for selecting nodes by various metrics, possibly coordinated with a node-link view of the network. In this paper, we present three novel approaches for achieving a tighter integration of these views through hybrid techniques for multidimensional visualization, graph selection and layout. First, we present the FlowVizMenu, a radial menu containing a scatterplot that can be popped up… Show more

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Cited by 81 publications
(58 citation statements)
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References 29 publications
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“…New graph theory algorithms for faster and biologically meaningful network layouts and algorithms for network structure analysis will need to be integrated into network visualization tools. Importantly, none of these algorithms would make a broad difference unless a user interface appropriate for biologists is available (Viau et al, 2010). Proteins are indispensable players in virtually all biological events.…”
Section: Discussionmentioning
confidence: 99%
“…New graph theory algorithms for faster and biologically meaningful network layouts and algorithms for network structure analysis will need to be integrated into network visualization tools. Importantly, none of these algorithms would make a broad difference unless a user interface appropriate for biologists is available (Viau et al, 2010). Proteins are indispensable players in virtually all biological events.…”
Section: Discussionmentioning
confidence: 99%
“…Relational data plus all attributes. To fully show a multi-dimensional network data, Viau et al [50] used a hybrid of scatterplots and node-link diagrams. GraphDice [6] combines node-link diagrams for representing social relationships, with a scatterplot matrix, for visualizing the multivariate attributes of entities in multiple small views.…”
Section: Hybrid Visualizationmentioning
confidence: 99%
“…By 128 , it provides two views of the data. One is an overview scatterplot matrix (seen on the left in Figure 16) which shows a small multiple of each possible combination of attributes while the other larger graph view to the right shows a full image of the graph based on the two currently selected attributes.…”
Section: Mapping Attributes Directly To Two-dimensional Spacementioning
confidence: 99%
“…GraphDice uses attributes that are application specific (e.g, in a co-authorship network, papers written, citation count, field) as well as node centrality metrics. Viau et al 128 also allow parts of the graph to be laid out manually or using a force-directed layout.…”
Section: Mapping Attributes Directly To Two-dimensional Spacementioning
confidence: 99%