Towards Better Perception of Graph Visualization

PhD Thesis Proposal Defence


Title: "Towards Better Perception of Graph Visualization"

by

Mr. Yong WANG


Abstract:

Graph data is ubiquitous in lots of application areas such as social media, 
biological networks, financial transactions and software engineering. To help 
users understand and analyze those graph data, the visualization community has 
been actively working on graph visualizations. Various graph visualization 
methods have been proposed in the past decades. However, due to the limited 
screen space, the unavoidable trade-off of different aesthetic criteria, human 
visual perceptual capability limit and others, users are not able to easily 
gain a comprehensive and accurate perception of graph visualization in all the 
situations, especially when the graph size increases. In this thesis, we 
propose novel approaches to enhance the user perception of both static and 
dynamic graph visualization.

For static graph visualization, prior studies have proved that it is impossible 
to optimize all the aesthetic criteria simultaneously. Ambiguity and other 
misleading information may always exist in the graph layout results. To provide 
users with an accurate and comprehensive perception of graph visualizations, we 
propose AmbiguityVis, a novel approach to inform users of the potential 
perception problems in the graph layout. More specifically, new readability 
metrics are proposed to quantify the ambiguities and heatmap-based 
visualizations are present to visualize those ambiguities. For dynamic graph 
visualization, we aim to enhance the perception of two major visualization ways 
of dynamic graphs, i.e., animation and small multiples. We first propose a 
vector field design approach to improve animated transitions of clustered 
objects. It explicitly enhances coordinated motion and avoids crowding, better 
supporting the tracking of individual objects and communities in a scene. Then, 
considering that the common uniform timeslicing can generate cluttered 
timeslices when edge bursts occur and empty timeslices when few interactions 
are present, we introduce a nonuniform timeslicing approach based on histogram 
equalization for small multiples. It divides the whole time range in a 
non-linear way and strikes a balance between temporal distortion of time 
dimension and similar visual complexity across intervals.


Date:			Wednesday, 23 May 2018

Time:                  	2:00pm - 4:00pm

Venue:                  Room 5508
                         lifts 25/26

Committee Members:	Prof. Huamin Qu (Supervisor)
  			Dr. Pedro Sander (Chairperson)
 			Dr. Yangqiu Song
 			Prof. Chiew-Lan Tai


**** ALL are Welcome ****