Opportunities and challenges in partitioning the graph measure space of real-world networks
This work addresses the challenge of partitioning graph measures for researchers in network science, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of identifying defining structural measures for different complex network domains using a dataset of thousands of real-world networks, and it successfully identified well-distinguishable groups with domain-specific features.
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.