A promising approach to compare graph clusterings is based on
using measurements for calculating the distance. Existing
measures either use the structure of clusterings or
quality--based aspects. Each approach suffers from critical
drawbacks. We introduce a new approach combining both aspects
and leading to better results for comparing graph clusterings.
An experimental evaluation of existing and new measures shows
that the significant drawbacks of existing techniques are not
only theoretical in nature and proves that the results of our
new measures are more coherent with intuition.