![](bgc.png)
pected the Edge Betweenness Centrality clustering al-
gorithm performs very well on structured graphs with
low density. However as the graphs become more
dense the agglomerative algorithm performs close to
the level of the centrality algorithm and by some met-
rics (MQ and clustering coefficient) it outperforms the
algorithm for graph with a density of d = 0.255, d
l
=
25.373. Given that the agglomerative approach is de-
signed to focus around nodes of interest to aid in vi-
sualisation rather than discover communities, we feel
our algorithm compares favourably with the Edge Be-
tweenness Centrality algorithm.
This paper has examined the effectiveness of the
clustering heuristics purely using calculated metrics.
Further evaluation is required using user experiments
to determine fully the effect of the clustering on graph
comprehensibility. Such an evaluation could also
be extended to cover examples of real-world graphs,
rather than large sets of procedurally created ones.
Further work is required concerning the layout of
these clusters and their visualisation. Currently when
visualising the graphs we use a simple force directed
layout of individual clusters, however a graph layout
with consideration given to inter-cluster edges to re-
duce edge crossing could be very beneficial. Node
hierarchies are frequently used to aid layout, so one
potential application of the above clustering approach
is to recursive apply it to generated cluster to generate
a hierarchy to aid in layout and in the routing of edges
within large graphs. The routing of edges between
and within clusters also impacts graph comprehensi-
bility, so an approach such as Holten’s hierarchical
edge bundling (Holten, 2006) may be useful here.
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VISUALISING SMALL WORLD GRAPHS - Agglomerative Clustering of Small World Graphs around Nodes of Interest
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