The DynNoSlice project focuses on developing methods for drawing and visualising dynamic multivariate graphs that are perceptually effective.
Multivariate graph visualisation is an emerging field in the areas of graph drawing and information visualisation. A multivariate graph is a graph whereby both nodes and edges of the network can have attributes. In a dynamic graph visualisation scenario, both structures and these attributes can change over time. Multivariate networks appear in many application areas such as social networks, biological networks, software engineering and intelligent infrastructure.
In this research, we focus on the development of rigorously evaluated techniques (both in terms of perceptual and computational efficiency) for the visualisation of dynamic multivariate networks.
We will pursue a number of objectives during this work:
- Create new algorithms that better support the cognitive map of the user for graphs that are structurally changing. More specifically, we will look at approaches that consider higher level components in the graph and known perceptual effects as crowding. More ambitious approaches involve algorithms that do not make use of timeslicing. The work involves the creation of graph drawing and visualisation techniques. The resulting software will be made available.
- In conjunction with the Oxford Internet Institute (OII) we plan to analyse dynamic multivariate graphs as related to dynamic and multivariate social networks.
This work is financed by the EPSRC First Grant EP/N005724/1.