A Space Efficient Clustered Visualization of Large Graphs
Mao Lin Huang and Quang Vinh Nguyen
Faculty of Information Technology, University of Technology, Sydney, Australia
This paper proposes a new technique for visualizing
large graphs of several ten thousands of vertices and edges.
To achieve the graph abstraction, a hierarchical clustered
graph is extracted from a general large graph based on the
community structures which are discovered in the graph.
An enclosure geometrical partitioning algorithm is then ap-
plied to achieve the space optimization. For graph drawing,
we technically use the combination of a spring-embbeder
algorithm and circular drawings that archives the goal of
optimization of display space and aesthetical niceness. We
also discuss an associated interaction mechanism accom-
panied with the layout solution. Our interaction not only al-
lows users to navigate hierarchically up and down through
the entire clustered graph, but also provides a way to nav-
igate multiple clusters concurrently. Animation is also im-
plemented to preserve users’ mental maps during the inter-
Graph visualization has been widely used in human-
computer interaction. A graph commonly includes a node
set and an edge set to represent entities and relationships
between entities respectively. In real-world applications,
graphs could be very large with thousands or perhaps mil-
lions of nodes, such as citation and collaboration networks
and the World Wide Web (WWW). As the result of rapid
increasing of the size in networks, the large scale visual-
ization has become one of the hottest topics in Information
Visualization. The question about how to comprehensively
display large graphs on the screen becomes the key issue in
graph visualization. However, the display of large graphs
can decrease significantly the performance of a visualiza-
tion technique which normally performs well on small or
medium size of datasets. Large graph visualization is usu-
ally suffered from poor running time and limitation of dis-
play space. In addition, the issue of ”view-ability” and us-
ability also arises because it will be almost impossible to
discern between nodes and edges when a dataset of thou-
sands of items are displayed .
It seems that classical graph models with a simple node-
link diagram tend to be inadequate for large scale visual-
ization with several thousands of items. The lack of formal
hierarchical structures in real world applications could limit
the conveying and perception of the complicated informa-
tion. Figure 1 shows an example of the graph visualization
of a WWW site which illustrates two typical major prob-
1. Too many nodes (pages) to be displayed and the layout
of such a large geometrical area could not be fitted in
one single screen
2. The layout of the graph has inefficient utilization of
display space with many unused areas in the display.
Figure 1. An example of a large graph visu-
alization using the classic virtual-page tech-
To solve the first problem, a well established new graph
model to accommodate with the visualization of large
graphs is required. We believe that one way to deal with
the display of large graphs is to provide users with a certain
degree of Graph Abstract. That is to filter out some details
Fourth International Conference on Image and Graphics
© 2007 IEEE
Figure 9. A navigational view of a multiple
sub-clusters from Figure 8, including the top-
center cluster, middle-center cluster, bottom-
left cluster and two bottom-left sub-clusters
from bottom-center cluster.
to tens thousands or even hundred thousands of elements
on an ordinary Personal Computer. Our method includes
two independent steps: clustering and visualization. The
clustering step aims to reduce the visual complexity and en-
hance the comprehension of large graph layouts through the
use of visual abstraction. We first discover an optimized
community structure in a graph and divide it into densely
connected clusters. We then use three levels of visual ab-
straction: 1)thefullcontextview, 2)thecurrentcontextview
and 3) the main view, to display the large graph. The visu-
alization uses a new space-efficient layout and navigation
technique that can visualize effectively the large clustered
graphs with several thousands of elements on a limited dis-
play space. We use a multiple view technique to archive the
focus+context view navigation. The interactive animation
is also employed to preserve the users’ mental maps during
This project is supported by Australia Research Council
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