On the Scalability of Large Graph Visualization

PhD Thesis Proposal Defence


Title: "On the Scalability of Large Graph Visualization"

by

Mr. Yanhong WU


Abstract:

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 proposal, 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
network. More specifically, we propose egoSlider, an interactive visual
analytical 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 and serve as the simple building
blocks of networks. We first conduct a comprehensive requirement analysis
for designing a visualization system that facilitates motif analysis in
dynamic networks. We then propose an interactive visualization system that
enables users to uncover the formation and evolution processes of network
motifs.


Date:			Thursday, 27 April 2017

Time:                  	10:00am - 12:00noon

Venue:                  Room 1504
                         (lifts 25/26)

Committee Members:	Prof. Huamin Qu (Supervisor)
  			Prof. Mordecai Golin (Chairperson)
 			Dr. Pedro Sander
 			Dr. Yangqiu Song


**** ALL are Welcome ****