THESIS
2018
xv, 103 pages : illustrations ; 30 cm
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 visualiza...[
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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.
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