Opportunities and challenges in partitioning the graph measure space of real-world networks

Abstract

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.

11 pages, 6 figures

References in corpus (8)

• Fast unfolding of communities in large networks
• Synchronization in complex networks
• Scale-free brain functional networks
• Explosive transitions in complex networks' structure and dynamics: percolation and synchronization
• The rich-club phenomenon across complex network hierarchies
• Characterizing the structural diversity of complex networks across domains
• Centrality Measures in Complex Networks: A Survey
• Degree distribution of complex networks from statistical mechanics principles