Leveraging advances in machine learning for the robust classification and interpretation of networks
The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often, simulation approaches involve selecting a suitable network generative model such as Erdös–Rényi or small-world. However, few tools are...
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| Format: | Article |
| Language: | English |
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The Royal Society
2025-04-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.240458 |
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| author | Raima Carol Appaw Nicholas M. Fountain-Jones Michael A. Charleston |
| author_facet | Raima Carol Appaw Nicholas M. Fountain-Jones Michael A. Charleston |
| author_sort | Raima Carol Appaw |
| collection | DOAJ |
| description | The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often, simulation approaches involve selecting a suitable network generative model such as Erdös–Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures and the formation of real-world networks. |
| format | Article |
| id | doaj-art-81fbf233c9104d359f7fcbb975d78723 |
| institution | Kabale University |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-81fbf233c9104d359f7fcbb975d787232025-08-20T03:51:59ZengThe Royal SocietyRoyal Society Open Science2054-57032025-04-0112410.1098/rsos.240458Leveraging advances in machine learning for the robust classification and interpretation of networksRaima Carol Appaw0Nicholas M. Fountain-Jones1Michael A. Charleston2Department of Mathematics, University of Tasmania College of Sciences and Engineering, Sandy Bay, Tasmania, AustraliaSchool of Natural Sciences, University of Tasmania, Hobart, Tasmania, AustraliaDepartment of Mathematics, University of Tasmania College of Sciences and Engineering, Sandy Bay, Tasmania, AustraliaThe ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often, simulation approaches involve selecting a suitable network generative model such as Erdös–Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures and the formation of real-world networks.https://royalsocietypublishing.org/doi/10.1098/rsos.240458network scienceclassificationmachine learninggenerative graph models |
| spellingShingle | Raima Carol Appaw Nicholas M. Fountain-Jones Michael A. Charleston Leveraging advances in machine learning for the robust classification and interpretation of networks Royal Society Open Science network science classification machine learning generative graph models |
| title | Leveraging advances in machine learning for the robust classification and interpretation of networks |
| title_full | Leveraging advances in machine learning for the robust classification and interpretation of networks |
| title_fullStr | Leveraging advances in machine learning for the robust classification and interpretation of networks |
| title_full_unstemmed | Leveraging advances in machine learning for the robust classification and interpretation of networks |
| title_short | Leveraging advances in machine learning for the robust classification and interpretation of networks |
| title_sort | leveraging advances in machine learning for the robust classification and interpretation of networks |
| topic | network science classification machine learning generative graph models |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.240458 |
| work_keys_str_mv | AT raimacarolappaw leveragingadvancesinmachinelearningfortherobustclassificationandinterpretationofnetworks AT nicholasmfountainjones leveragingadvancesinmachinelearningfortherobustclassificationandinterpretationofnetworks AT michaelacharleston leveragingadvancesinmachinelearningfortherobustclassificationandinterpretationofnetworks |