{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:04:54Z","timestamp":1764785094813,"version":"3.41.2"},"reference-count":33,"publisher":"World Scientific Pub Co Pte Ltd","issue":"15","funder":[{"name":"China-Asean Vocational Education Research Center in 2021","award":["ZGDMKT2021ZD012"],"award-info":[{"award-number":["ZGDMKT2021ZD012"]}]},{"name":"Key subject of Science and Technology Development Center of Ministry of Education","award":["2020ITA03005"],"award-info":[{"award-number":["2020ITA03005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:p> With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods. <\/jats:p>","DOI":"10.1142\/s0218126623502626","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T07:04:40Z","timestamp":1678431880000},"source":"Crossref","is-referenced-by-count":3,"title":["A Graph Neural Network-Based Digital Assessment Method for Vocational Education Level of Specific Regions"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7930-4992","authenticated-orcid":false,"given":"Weitai","family":"Luo","sequence":"first","affiliation":[{"name":"Guangxi Technological College of Machinery and Electricity, Guangxi 530000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haining","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Finance and Economics, Hainan Vocational University of Science and Technology, Hainan 571126, P. R. 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