Recently, edge bundling methods became popular for visualising large dense networks, however,
most of previous work mainly relies on geometry to define spatial compatibility between the edges.
We proposed a new framework for edge bundling, which tightly integrates topology, geometry and
importance. In particular, we have introduced new edge compatibility measures, called importance
compatibility and topology compatibility. Based on the framework, we presented four variations of
forcedirected edge bundling method: Centralitybased bundling, Radial bundling, Topologybased
bundling, and Orthogonal bundling.
Our experimental results using social networks, biological networks, geographic networks and
clustered graphs indicated that our new framework can be very useful to highlight the most important
topological skeletal structures of the input network. For example, our radial bundling has proved to
highlight significant functional groups in biological networks. In fact, our visualisation guided
biologists to derive new biological hypothesis, and currently laboratory experiments are being
conducted to confirm their new hypothesis. 
Quan Nguyen, SeokHee Hong, Peter Eades, "TGIEB: A New Framework for Edge Bundling
integrating Topology, Geometry and Importance", Proceeding of GD 2011 (International
Symposium on Graph Drawing 2011).
Edge bundling methods became popular for visualising large dense networks; however, most of previous work mainly relies on geometry to define compatibility between the edges.
In this paper, we present a new framework for edge bundling, which tightly integrates topology, geometry and importance. In particular, we introduce new edge compatibility measures, namely importance compatibility and topology compatibility. More specifically, we present four variations of force directed edge bundling method based on the framework: Centralitybased bundling, Radial bundling, Topologybased bundling, and Orthogonal bundling.
Our experimental results with social networks, biological networks, geographic networks and clustered graphs indicate that our new framework can be very useful to highlight the most important topological skeletal structures of the input networks.
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S. Janowski, B. Kormeier, K. Hippe, Q. Nguyen, S. Hong, R. Hofestädt, J. Stoye, B.
Kaltschmidt and C. Kaltschmidt, "Reconstruction and analysis of biological networks based on
large scale data from the NFκB pathway", Proceedings of IB 2011 (International Symposium
on Integrative Bioinformatics 2011), to appear.
