2014
Most graph visualisation techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, this work presents a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation, users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” The technique was used to study transition graphs that model real-world systems. A later book chapter enumerates the types of tasks people perform on multivariate networks. The approach is an offshoot of doctoral research at Eindhoven University of Technology.