Dual Adjacency Matrix: Exploring Link Groups in Dense Networks

• Published in: Computer graphics forum Vol. 34; no. 3; pp. 311 - 320
• Main Authors: Dinkla, K., Riche, N. Henry, Westenberg, M.A.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2015
• Subjects: Aids; Analysis; Categories and Subject Descriptors (according to ACM CCS); Communications networks; Communities; Computer graphics; Computer Graphics [I.3.3]: Picture/Image Generation-Line and curve generation; Correlation analysis; Information science; Links; Networks; Neurosciences; Prototypes; Scientists; Studies; Visualization
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12643

Node 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.