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To model the correlations between the output variables, the evaluation data are initially transformed from their traditional two-dimensional format into a graph-structured format based on the distance correlation coefficients. Then, graph isomorphism networks (GINs) are employed to achieve GRL and evaluation data classification. The graph embeddings produced by GRL, which represent the interdependencies and dynamic evolutionary patterns among variables, enable the categorization of reference data as originating from one of the alternative models. Finally, the most credible simulation model is determined based on the category probabilities of the multisample reference data. The effectiveness of the proposed method in feature extraction and model selection is demonstrated through an application example on aerodynamic parameter models of a flight vehicle.<\/jats:p>","DOI":"10.1177\/00375497241305832","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T07:26:28Z","timestamp":1735802788000},"page":"493-505","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["An intelligent model selection method based on graph representation learning"],"prefix":"10.1177","volume":"101","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5828-198X","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"Control and Simulation Center, Harbin Institute of Technology, China"},{"name":"National Key Laboratory of Complex System Modeling and Simulation, China"}]},{"given":"Ping","family":"Ma","sequence":"additional","affiliation":[{"name":"Control and Simulation Center, Harbin Institute of Technology, China"},{"name":"National Key Laboratory of Complex System Modeling and Simulation, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Control and Simulation Center, Harbin Institute of Technology, China"},{"name":"National Key Laboratory of Complex System Modeling and Simulation, China"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Control and Simulation Center, Harbin Institute of Technology, China"},{"name":"National Key Laboratory of Complex System Modeling and Simulation, China"}]}],"member":"179","published-online":{"date-parts":[[2025,1]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-137-32803-8"},{"key":"e_1_3_3_3_2","first-page":"1","volume-title":"Simulation modeling and analysis","author":"Law AW","year":"2000","unstructured":"Law AW, Kelton WD. 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Palo Alto, California: AAAI Press."},{"key":"e_1_3_3_44_2","first-page":"2769","article-title":"Measuring and testing dependence by correlation of distance","volume":"35","author":"Szekely GJ","year":"2008","unstructured":"Szekely GJ, Rizzo ML, Bakirov NK. Measuring and testing dependence by correlation of distance. Ann Stat 2008; 35: 2769\u20132794.","journal-title":"Ann Stat"},{"key":"e_1_3_3_45_2","first-page":"1","volume-title":"Introduction to graph theory","author":"West DB.","year":"2001","unstructured":"West DB. Introduction to graph theory. 2nd ed. Hoboken, NJ: Prentice Hall, 2001, pp. 1\u20132.","edition":"2"},{"key":"e_1_3_3_46_2","first-page":"4800","volume-title":"Proceedings of the 32nd international conference on neural information processing systems (NIPS 2018) (Advances in neural information processing systems)","author":"Ying R","unstructured":"Ying R, You J, Morris C, et al. Hierarchical graph representation learning with differentiable pooling. In: Proceedings of the 32nd international conference on neural information processing systems (NIPS 2018) (Advances in neural information processing systems), Montr\u00e9al, QC, Canada, 3\u20138 December 2018, pp. 4800\u20134810. Red Hook, NY: Curran Associates."},{"key":"e_1_3_3_47_2","first-page":"4438","volume-title":"Proceedings of the 32nd AAAI conference on artificial intelligence","author":"Zhang M","unstructured":"Zhang M, Cui Z, Neumann M, et al. An end-to-end deep learning architecture for graph classification. In: Proceedings of the 32nd AAAI conference on artificial intelligence, New Orleans, LA, 2\u20137 February 2018, pp. 4438\u20134445. Palo Alto, California: AAAI Press."},{"key":"e_1_3_3_48_2","first-page":"1953","volume-title":"Proceedings of the 2021 IEEE 18th international symposium on biomedical imaging (ISBI)","author":"Cai C","unstructured":"Cai C, Xu D, Fang C, et al. Graph Neural Networks for the cross-domain histopathological image classification. 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