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dc.contributor.authorDinkla, Kasperen_US
dc.contributor.authorHenry-Riche, Nathalieen_US
dc.contributor.authorWestenberg, Michel A.en_US
dc.contributor.editorH. Carr, K.-L. Ma, and G. Santuccien_US
dc.date.accessioned2015-05-22T12:51:41Z
dc.date.available2015-05-22T12:51:41Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12643en_US
dc.description.abstractNode grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high-quality overlapping node communities. There is a conceptual gap between node groups and link groups that poses an interesting visualization challenge. We introduce the Dual Adjacency Matrix to bridge this gap. This matrix combines node and link group techniques via a generalization that also enables it to be coordinated with a node-link-contour diagram. These methods have been implemented in a prototype that we evaluated with an information scientist and neuroscientist via interviews and prototype walk- throughs. We demonstrate this prototype with the analysis of a trade network and an fMRI correlation network.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputer Graphics [I.3.3]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectLine and curve generationen_US
dc.titleDual Adjacency Matrix: Exploring Link Groups in Dense Networksen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.sectionheadersGraphsen_US
dc.description.volume34en_US
dc.description.number3en_US
dc.identifier.doi10.1111/cgf.12643en_US
dc.identifier.pages311-320en_US


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