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UBC Theses and Dissertations
Feature-based graph visualization Archambault, Daniel William
Abstract
A graph consists of a set and a binary relation on that set. Each element of the set is a node of the graph, while each element of the binary relation is an edge of the graph that encodes a relationship between two nodes. Graph are pervasive in many areas of science, engineering, and the social sciences: servers on the Internet are connected, proteins interact in large biological systems, social networks encode the relationships between people, and functions call each other in a program. In these domains, the graphs can become very large, consisting of hundreds of thousands of nodes and millions of edges. Graph drawing approaches endeavour to place these nodes in two or three-dimensional space with the intention of fostering an understanding of the binary relation by a human being examining the image. However, many of these approaches to drawing do not exploit higher-level structures in the graph beyond the nodes and edges. Frequently, these structures can be exploited for drawing. As an example, consider a large computer network where nodes are servers and edges are connections between those servers. If a user would like understand how servers at UBC connect to the rest of the network, a drawing that accentuates the set of nodes representing those servers may be more helpful than an approach where all nodes are drawn in the same way. In a feature-based approach, features are subgraphs exploited for the purposes of drawing. We endeavour to depict not only the binary relation, but the high-level relationships between features. This thesis extensively explores a feature-based approach to graph vi sualization and demonstrates the viability of tools that aid in the visual ization of large graphs. Our contributions lie in presenting and evaluating novel techniques and algorithms for graph visualization. We implement five systems in order to empirically evaluate these techniques and algorithms, comparing them to previous approaches.
Item Metadata
Title |
Feature-based graph visualization
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2008
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Description |
A graph consists of a set and a binary relation on that set. Each element
of the set is a node of the graph, while each element of the binary relation
is an edge of the graph that encodes a relationship between two nodes.
Graph are pervasive in many areas of science, engineering, and the social
sciences: servers on the Internet are connected, proteins interact in large
biological systems, social networks encode the relationships between people,
and functions call each other in a program. In these domains, the graphs
can become very large, consisting of hundreds of thousands of nodes and
millions of edges.
Graph drawing approaches endeavour to place these nodes in two or
three-dimensional space with the intention of fostering an understanding
of the binary relation by a human being examining the image. However,
many of these approaches to drawing do not exploit higher-level structures
in the graph beyond the nodes and edges. Frequently, these structures can
be exploited for drawing. As an example, consider a large computer network
where nodes are servers and edges are connections between those servers.
If a user would like understand how servers at UBC connect to the rest of
the network, a drawing that accentuates the set of nodes representing those
servers may be more helpful than an approach where all nodes are drawn in
the same way. In a feature-based approach, features are subgraphs exploited
for the purposes of drawing. We endeavour to depict not only the binary
relation, but the high-level relationships between features.
This thesis extensively explores a feature-based approach to graph vi
sualization and demonstrates the viability of tools that aid in the visual
ization of large graphs. Our contributions lie in presenting and evaluating
novel techniques and algorithms for graph visualization. We implement five
systems in order to empirically evaluate these techniques and algorithms,
comparing them to previous approaches.
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Extent |
8716543 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2008-12-04
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0051247
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2008-11
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International