Reconstructing supply networks
Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by pa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Journal of Physics: Complexity |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-072X/ad30bf |
| _version_ | 1827308503228743680 |
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| author | Luca Mungo Alexandra Brintrup Diego Garlaschelli François Lafond |
| author_facet | Luca Mungo Alexandra Brintrup Diego Garlaschelli François Lafond |
| author_sort | Luca Mungo |
| collection | DOAJ |
| description | Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda. |
| first_indexed | 2024-04-24T19:08:52Z |
| format | Article |
| id | doaj.art-d68ee728dd3d4fe9bb4f208b78ab8fd0 |
| institution | Directory Open Access Journal |
| issn | 2632-072X |
| language | English |
| last_indexed | 2024-04-24T19:08:52Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj.art-d68ee728dd3d4fe9bb4f208b78ab8fd02024-03-26T13:57:13ZengIOP PublishingJournal of Physics: Complexity2632-072X2024-01-015101200110.1088/2632-072X/ad30bfReconstructing supply networksLuca Mungo0https://orcid.org/0000-0002-5007-947XAlexandra Brintrup1https://orcid.org/0000-0002-4189-2434Diego Garlaschelli2https://orcid.org/0000-0001-6035-1783François Lafond3https://orcid.org/0000-0002-8333-561XInstitute for New Economic Thinking at the Oxford Martin School, University of Oxford , Oxford, United Kingdom; Mathematical Institute, University of Oxford , Oxford, United Kingdom; Macrocosm, Inc. , New York, NY, United States of AmericaInstitute for Manufacturing, Department of Engineering, University of Cambridge , Cambridge, United KingdomIMT School of Advanced Studies , Lucca, Italy; Lorentz Institute for Theoretical Physics, University of Leiden , Leiden, The NetherlandsInstitute for New Economic Thinking at the Oxford Martin School, University of Oxford , Oxford, United Kingdom; Smith School of Enterprise and the Environment, University of Oxford , Oxford, United KingdomNetwork reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda.https://doi.org/10.1088/2632-072X/ad30bflink predictionsupply networksvalue chainsmaximum entropymachine learning |
| spellingShingle | Luca Mungo Alexandra Brintrup Diego Garlaschelli François Lafond Reconstructing supply networks Journal of Physics: Complexity link prediction supply networks value chains maximum entropy machine learning |
| title | Reconstructing supply networks |
| title_full | Reconstructing supply networks |
| title_fullStr | Reconstructing supply networks |
| title_full_unstemmed | Reconstructing supply networks |
| title_short | Reconstructing supply networks |
| title_sort | reconstructing supply networks |
| topic | link prediction supply networks value chains maximum entropy machine learning |
| url | https://doi.org/10.1088/2632-072X/ad30bf |
| work_keys_str_mv | AT lucamungo reconstructingsupplynetworks AT alexandrabrintrup reconstructingsupplynetworks AT diegogarlaschelli reconstructingsupplynetworks AT francoislafond reconstructingsupplynetworks |