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

Józsa, Máté, Lázár, Alpár S. and Lázár, Zsolt Iosif (2021) Opportunities and challenges in partitioning the graph measure space of real-world networks. Journal of Complex Networks, 9 (2). ISSN 2051-1329

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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.

Item Type: Article
Additional Information: Published online: 26 August 2021
Uncontrolled Keywords: discriminating features,network classification,network dataset,null models,real-world networks,computer networks and communications,management science and operations research,control and optimization,computational mathematics,applied mathematics ,/dk/atira/pure/subjectarea/asjc/1700/1705
Faculty \ School: Faculty of Medicine and Health Sciences > School of Health Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 22 Jul 2021 00:06
Last Modified: 22 Sep 2021 01:57
URI: https://ueaeprints.uea.ac.uk/id/eprint/80681
DOI: 10.1093/comnet/cnab006

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