Product Space Clustering with Graph Learning for Diversifying Industrial Production
During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/7/2833 |
| _version_ | 1797212932060217344 |
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| author | Kévin Cortial Adélaïde Albouy-Kissi Frédéric Chausse |
| author_facet | Kévin Cortial Adélaïde Albouy-Kissi Frédéric Chausse |
| author_sort | Kévin Cortial |
| collection | DOAJ |
| description | During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research. |
| first_indexed | 2024-04-24T10:50:14Z |
| format | Article |
| id | doaj.art-b20090823f364467a3afe3fcb5cc6bd5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:50:14Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b20090823f364467a3afe3fcb5cc6bd52024-04-12T13:14:57ZengMDPI AGApplied Sciences2076-34172024-03-01147283310.3390/app14072833Product Space Clustering with Graph Learning for Diversifying Industrial ProductionKévin Cortial0Adélaïde Albouy-Kissi1Frédéric Chausse2Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, FranceUniversité Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, FranceUniversité Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, FranceDuring economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research.https://www.mdpi.com/2076-3417/14/7/2833graph neural networkscommunity detectionproduct space |
| spellingShingle | Kévin Cortial Adélaïde Albouy-Kissi Frédéric Chausse Product Space Clustering with Graph Learning for Diversifying Industrial Production Applied Sciences graph neural networks community detection product space |
| title | Product Space Clustering with Graph Learning for Diversifying Industrial Production |
| title_full | Product Space Clustering with Graph Learning for Diversifying Industrial Production |
| title_fullStr | Product Space Clustering with Graph Learning for Diversifying Industrial Production |
| title_full_unstemmed | Product Space Clustering with Graph Learning for Diversifying Industrial Production |
| title_short | Product Space Clustering with Graph Learning for Diversifying Industrial Production |
| title_sort | product space clustering with graph learning for diversifying industrial production |
| topic | graph neural networks community detection product space |
| url | https://www.mdpi.com/2076-3417/14/7/2833 |
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