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ToolsJózsa, 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 |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Groups > Dementia & Complexity in Later Life Faculty of Medicine and Health Sciences > Research Centres > Lifespan Health |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 22 Jul 2021 00:06 |
| Last Modified: | 19 Oct 2023 03:00 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/80681 |
| DOI: | 10.1093/comnet/cnab006 |
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