Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity

Abstract

HKUST Figure 1: Comparison of uniform timeslicing and non-uniform timeslicing using the Rugby Dataset. (a) Traditional uniform timeslicing, (b) non-uniform timeslicing. The whole stream of temporal edges are divided into 12 intervals with the sequence number marked at the top right corner of each snapshot. The top left bars and smoothed line chart show the time range of each interval (in a format of "year.month.day ") and the edge frequency distribution, respectively. The dotted blue rectangles highlight two interesting intervals and the red dotted ellipses highlight several thick edges. The teams are: ca -Cardiff, sc -Scarlets, dr -Dragons, os -Ospreys, le -Leinster, mu -Munster, ul -Ulster, co -Connacht, ed -Edinburgh, gl -Glasgow, ze -Zebre, and be -Benetton. ABSTRACTUniform 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 timeslices 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 nonuni-* form 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.

The Effectiveness of Interactive Visualization Techniques for Time Navigation of Dynamic Graphs on Large Displays

“…The majority of these experiments have focused on the structural properties of the network first and the time navigation second. Also, all the above experiments assumed a uniform timeslicing selected beforehand whereas many recent techniques for graph visualization do not make this assumption [44,57,58,64]. For dynamic graphs that are long in time (for example, events lasting seconds over months of data), no experiments have been run.…”

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

“…The uniformity of timeslices poses a problem as the distribution of transmission events is unequal over time. Event-based methods [41,50] for the visualization of dynamic networks [35,63,64,78] are more applicable. However, they have not been designed for outbreak networks visualization in a way that fulfills the needs of the infection control experts.…”

Interactive Time-Series of Measures for Exploring Dynamic Networks

“…For example, in MultiPiles [5], a dynamic network can be sliced either in a greedy manner, grouping neighboring snapshots that are similar in the adjacent matrices, or through userdefined manual slicing like cutting a video clip. More recently, work from Wang et al [33] proposed to equalize the number of temporal links and derive cutting points that promote to find snapshots with consistent scales. The process is similar to the histogram equalization technique in digital image processing.…”