  Based on a large dataset containing thousands of real-world networks ranging
from genetic, protein interaction, and metabolic networks to brain, language,
ecology, and social networks we search for defining structural measures of the
different complex network domains (CND). We calculate 208 measures for all
networks and using a comprehensive and scrupulous workflow of statistical and
machine learning methods we investigated the limitations and possibilities of
identifying the key graph measures of CNDs. Our approach managed to identify
well distinguishable groups of network domains and confer their relevant
features. These features turn out to be CND specific and not unique even at the
level of individual CNDs. The presented methodology may be applied to other
similar scenarios involving highly unbalanced and skewed datasets.
