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

TL;DR

This study analyzes a large dataset of real-world networks to identify structural measures that distinguish different complex network domains, revealing domain-specific features and demonstrating a methodology applicable to unbalanced datasets.

Contribution

It introduces a comprehensive workflow combining statistical and machine learning methods to identify key graph measures that differentiate complex network domains.

Findings

Successfully distinguished network domains based on structural measures

Identified domain-specific features that are not unique to individual domains

Methodology applicable to other unbalanced, skewed datasets

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.