Inference for Graphs and Networks
27
partition is assumed known. In this vein Danon
et al.
(2005) specified an
explicit probability model for structure and compared how well different
graph partitioning schemes recovered the true subgroups of data, rank-
ing them by both execution time as well as average distance between true
and found partitions. Gustafsson
et al.
(2006) performed a similar com-
parison, along with a study of differences in “found” partitions between
algorithms for several well-known data sets, including the karate club data
of Section 1.4.1. They found that standard clustering algorithms (e.g.,
k
-means) sometimes outperform more specialized network partition algo-
rithms. Finally, Fortunato and Barth´
elemy (2007) have undertaken theoret-
ical investigations of the sensitivity and power of a particular partitioning
algorithm to detect subgroups below a certain size.
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