Visualizing Dynamic Interactions
Institut National de Recherche en Informatique et Automatique (INRIA) Saclay - ile-de-France
OVERVIEW: Graphs are powerful mathematical structures for modeling and representing many natural phenomena. In trying to explore and make sense of graphs collected in the wild — such as social interactions stored by social network sites or correlations between brain signals obtained using fMRI — visualization is often used. However, traditional visualization techniques are limited to sparse graphs: dense graphs are unreadable. Much progress has been made recently using matrix-based and hybrid visualizations to explore large and dense networks. Although understanding the visualization of the adjacency matrix of a graph is not as immediate as the traditional node-link representation, it does not suffer from most of its drawbacks and only takes a few minutes to grasp, a very reasonable time considering its expressive power. I’ll show how this relatively novel representation can be used to visualize many types of graphs, even dynamic graphs, with no limitation on density and good scalability. I'll show some results on social networks and brain signals.
READINGS:Wybrow, M., Elmqvist, N., Fekete, J. D., von Landesberger, T., van Wijk, J. J., & Zimmer, B. (2014). Interaction in the Visualization of Multivariate Networks. In Multivariate Network Visualization (pp. 97-125). Springer International Publishing.
Bach, B., Pietriga, E., & Fekete, J. D. (2014, April). Visualizing Dynamic Networks with Matrix Cubes. In SICCHI Conference on Human Factors in Computing Systems (CHI).