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Description:
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.