Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Quality Metric for Visualization of Clusters in Graphs   Traditionally, graph quality metrics focus on readability, but recent studies
show the need for metrics which are more specific to the discovery of patterns
in graphs. Cluster analysis is a popular task within graph analysis, yet there
is no metric yet explicitly quantifying how well a drawing of a graph
represents its cluster structure. We define a clustering quality metric
measuring how well a node-link drawing of a graph represents the clusters
contained in the graph. Experiments with deforming graph drawings verify that
our metric effectively captures variations in the visual cluster quality of
graph drawings. We then use our metric to examine how well different graph
drawing algorithms visualize cluster structures in various graphs; the results
con-firm that some algorithms which have been specifically designed to show
cluster structures perform better than other algorithms.

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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an