Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity

TL;DR

This paper introduces a nonuniform timeslicing method for dynamic graph visualization that balances visual complexity across timeslices by adapting histogram equalization, improving clarity over traditional uniform slicing.

Contribution

It proposes a novel nonuniform timeslicing approach based on visual complexity, enhancing dynamic graph visualization by better representing important temporal features.

Findings

Balances visual complexity across timeslices

Improves clarity compared to uniform slicing

Effectively highlights bursting edge events

Abstract

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 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 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.