Many developments have recently been made in mining dynamic networks; however, effective visualization of dynamic networks remains a significant challenge. Dynamic networks are typically visualized via a sequence of static graph layouts. In addition to providing a visual representation of the network topology at each time step, the sequence should preserve the “mental map” between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network and gain valuable insights that are difficult to convey by summary statistics alone. We propose two regularized layout algorithms for visualizing dynamic networks, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). These algorithms discourage node positions from moving drastically between time steps and encourage nodes to be positioned near other members of their group. We apply the proposed algorithms on several data sets to illustrate the benefit of the regularizers for producing interpretable visualizations.
K. S. Xu, M. Kliger, and A. O. Hero III. Visualizing the temporal evolution of dynamic networks. In Proc. 9th Workshop on Mining and Learning with Graphs, 2011. (.pdf)
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Evolving stochastic block model
DMDS layouts with both regularizers View animation (0.5 MB)
DMDS layouts with only temporal regularization View animation (0.5 MB)
DMDS layouts with only group regularization View animation (0.5 MB)
DMDS layouts with no regularization View animation (0.5 MB)
MIT Reality Mining
DMDS layouts with both regularizers View animation (10.4 MB, allow time to load!)