THESIS
2014
xi, 44 pages : illustrations ; 30 cm
Abstract
The discovery and analysis of community structures have become an important topic
in the fields of biology, society and computer science. In recent years, a lot of methods
have been proposed to describe various community properties such as conductance and
modularity. The study of community relations also reveals different community interaction
patterns. Visualization has been widely used to help people better understand major
communities in a large network. However, existing visualization methods mainly focus
on depicting community structures themselves while community characteristics and fuzzy
relations between communities are ignored.
In this thesis, we introduce a novel visualization method which allows people to explore,
compare and refine communities. First, major commun...[
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The discovery and analysis of community structures have become an important topic
in the fields of biology, society and computer science. In recent years, a lot of methods
have been proposed to describe various community properties such as conductance and
modularity. The study of community relations also reveals different community interaction
patterns. Visualization has been widely used to help people better understand major
communities in a large network. However, existing visualization methods mainly focus
on depicting community structures themselves while community characteristics and fuzzy
relations between communities are ignored.
In this thesis, we introduce a novel visualization method which allows people to explore,
compare and refine communities. First, major communities in a large network
are detected using data mining and community analysis methods. Then, the statistics
and aggregation attributes of each community, the relational strength between major
communities, and the important boundary nodes connecting those communities can be
computed and stored. We propose a novel method based on Voronoi treemap to encode
each community with a polygon and the relative positions of polygons encode their relational
strengths. Different community attributes can be encoded by polygon shapes,
sizes and colors. A corner-cutting method is further proposed to adjust the smoothness
of polygons based on certain community attribute. To accommodate the boundary nodes
connecting major communities, the gaps between the polygons are widened by a polygon-shrinking
algorithm such that the boundary nodes can be conveniently embedded into
the newly created spaces. The method is very efficient, enabling users to test different
community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with real data demonstrate that our
visualization approach can provide a visual summary of major communities in a large
network, and help people better understand the characteristics of each community and
inspect various relational patterns between communities effectively.
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