Visualizing time-varying matrices using multidimensional scaling and reorderable matrices

We present a novel approach to visualize time-varying matrices. This approach is based on combining multidimensional scaling and the reorderable matrix method. An adapted version of multidimensional scaling which allows the construction of similarity plots for columns/rows of time-varying matrices is proposed. In addition, we have extended the reorderable matrix method to allow the visual exploration of time-varying matrix data in a tabular form for being able to verify the results of MDS and possibly discover new patterns in data. The benefits of our approach are illustrated by showing visualizations of sensitivity matrices generated during simulations of metabolic network models.