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

Máté Józsa, Alpár S. Lázár, Zsolt Iosif Lázár

Research output: Contribution to journalArticle

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.
Original languageEnglish
Article numbercnab006
JournalJournal of Complex Networks
Volume9
Issue number2
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Discriminating features
  • Network classification
  • Network dataset
  • Null models
  • Real-world networks

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