As a natural representation of data, graph structures exist in many domains such as finance, sociology, biology, and software engineering. Visualization techniques have been widely utilized to facilitate graph analysis by taking advantages of human’s strong ability in visual perception. One of the most critical problems in graph visualization is scalability. Common graph visualization techniques do not scale well when the graph size increases to a certain degree, which prevents people from gaining insights into graphs. In this thesis, we aim to better understand and to solve the scalability problem in visualizing both static and dynamic graphs.

Our first work investigates the performance of different graph sampling algorithms in the perspective of visualization. We first conduct...[ Read more ]

As a natural representation of data, graph structures exist in many domains such as finance, sociology, biology, and software engineering. Visualization techniques have been widely utilized to facilitate graph analysis by taking advantages of human’s strong ability in visual perception. One of the most critical problems in graph visualization is scalability. Common graph visualization techniques do not scale well when the graph size increases to a certain degree, which prevents people from gaining insights into graphs. In this thesis, we aim to better understand and to solve the scalability problem in visualizing both static and dynamic graphs.

Our first work investigates the performance of different graph sampling algorithms in the perspective of visualization. We first conduct a pilot study to identify the important visual factors that need to be preserved after sampling from a visualization perspective. Then we conduct three controlled within-subject experiments to evaluate the performance of five common graph sampling algorithms in preserving these visual factors. After comparing and discussing our results with previous metric evaluation results, we propose several recommendations for selecting sampling algorithms in graph visualization.

The second work studies the evolution process of dynamic egocentric networks. More specifically, we propose egoSlider, an interactive visual analytics system that helps people explore, compare, and analyze dynamic egocentric network evolution in three hierarchical levels. The proposed technique is evaluated by two usage scenarios using an academic collaboration network and an e-mail communication network. Also, a controlled user study indicates that egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

In the third work, we focus on network motifs, which are defined as small connected and induced subgraph patterns that serve as the simple building blocks of networks. We introduce an interactive visualization system that enables users to uncover the formation and evolution processes of network motifs. A usage scenario and a qualitative user study have also been conducted to demonstrate the effectiveness of the proposed method.