An illustration of the proposed nonuniform timeslicing of dynamic graph based on visual complexity. The top and bottom his- tograms show the original histogram of a dynamic graph dataset and the histogram equalization result, respectively. By dividing the bins evenly in the histogram equalization result, we balance the visual com- plexity across intervals, enhancing the detailed exploration for time periods with bursting edges while coarsening the periods with sparse edges, as shown by the two graphs of the first and last intervals.


Uniform timeslicing of dynamic graphs has been used due to its convenience and uniformity across the time dimension. However, uniform timeslicing does not take the data set into account, which can generate cluttered timeslices with edge bursts and empty times- lices with few interactions. The graph mining filed has explored nonuniform timeslicing methods specifically designed to preserve graph features for mining tasks. In this paper, we propose a nonuniform timeslicing approach for dynamic graph visualization. Our goal is to create timeslices of equal visual complexity. To this end, we adapt histogram equalization to create timeslices with a similar number of events, balancing the visual complexity across timeslices and conveying more important details of timeslices with bursting edges. A case study has been conducted, in comparison with uniform timeslicing, to demonstrate the effectiveness of our approach.


Yong Wang, Daniel Archambault, Hammad Haleem, Torsten Moeller, Yanhong Wu, Huamin Qu. "Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity." In Proceedings of IEEE VIS 2019 (Short Paper), 2019. In press.

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