Large dynamic graphs visualization
In this work, we present a new approach to exploring dynamic graphs. We have developed a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to define two visual representations. The first view is an overview that shows how clusters evolve over time and provides an interface to find and select interesting time-steps. The second view consists of a node link diagram of a selected time-step which uses the clustering to efficiently define the layout. By using the time-dependant clustering, we ensure the stability of our visualization and preserve user mental map by minimizing node motion. Also, as the clustering is computed ahead of time, the second view updates in linear time which allows for interactivity even for graphs with upwards of tens of thousands of nodes.
We have also developped an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.
A. Sallaberry, C. Muelder, K.-L. Ma. Clustering, visualizing, and navigating for large dynamic graphs. Proceedings of the 20th International Symposium on Graph Drawing (GD 2012), W. Didimo and M. Patrignani (Eds.), LNCS 7704, pp. 487-498, Springer-Verlag Berlin Heidelberg, 2013.
C. Muelder, T. Crnovrsanin, A. Sallaberry, K.-L. Ma. Egocentric Storylines for Visual Analysis of Large Dynamic Graphs. Proceedings of the 1st IEEE Workshop on Big Data Visualization (BigDataVis 2013), 2013.
Last update on 20/01/2015