{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T05:56:09Z","timestamp":1716357369497},"reference-count":140,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T00:00:00Z","timestamp":1700784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks. This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, and opportunities. We discuss how GNNs and NDTs can be leveraged to improve network performance, optimize routing, enable network slicing, and enhance security in future networks. Additionally, we highlight certain advantages of incorporating GNNs into NDTs and present two case studies. Finally, we address the key challenges and promising directions in the field, aiming to inspire further advancements and foster innovation in GNN-based NDTs for future networks.<\/jats:p>","DOI":"10.3390\/fi15120377","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T08:35:06Z","timestamp":1701074106000},"page":"377","source":"Crossref","is-referenced-by-count":1,"title":["Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0842-7282","authenticated-orcid":false,"given":"Duc-Thinh","family":"Ngo","sequence":"first","affiliation":[{"name":"Orange Innovation, 35510 Cesson-S\u00e9vign\u00e9, France"},{"name":"IMT Atlantique, Nantes University, \u00c9cole Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France"}]},{"given":"Ons","family":"Aouedi","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Nantes University, \u00c9cole Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4350-0254","authenticated-orcid":false,"given":"Kandaraj","family":"Piamrat","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Nantes University, \u00c9cole Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2912-1984","authenticated-orcid":false,"given":"Thomas","family":"Hassan","sequence":"additional","affiliation":[{"name":"Orange Innovation, 35510 Cesson-S\u00e9vign\u00e9, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5605-2262","authenticated-orcid":false,"given":"Philippe","family":"Raipin-Parv\u00e9dy","sequence":"additional","affiliation":[{"name":"Orange Innovation, 35510 Cesson-S\u00e9vign\u00e9, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCOM.001.2200012","article-title":"Network Digital Twin: Context, Enabling Technologies, and Opportunities","volume":"60","author":"Almasan","year":"2022","journal-title":"IEEE Commun. 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