{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T04:20:23Z","timestamp":1778127623152,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["72061006"],"award-info":[{"award-number":["72061006"]}]},{"name":"National Natural Science Foundation of China","award":["Qiankehejichu-ZK[2024] General 424"],"award-info":[{"award-number":["Qiankehejichu-ZK[2024] General 424"]}]},{"name":"National Natural Science Foundation of China","award":["Qianshixinmiao-[2021]A30"],"award-info":[{"award-number":["Qianshixinmiao-[2021]A30"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["72061006"],"award-info":[{"award-number":["72061006"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["Qiankehejichu-ZK[2024] General 424"],"award-info":[{"award-number":["Qiankehejichu-ZK[2024] General 424"]}]},{"name":"Guizhou Provincial Basic Research Program (Natural Science)","award":["Qianshixinmiao-[2021]A30"],"award-info":[{"award-number":["Qianshixinmiao-[2021]A30"]}]},{"name":"Academic New Seedling Foundation Project of Guizhou Normal University","award":["72061006"],"award-info":[{"award-number":["72061006"]}]},{"name":"Academic New Seedling Foundation Project of Guizhou Normal University","award":["Qiankehejichu-ZK[2024] General 424"],"award-info":[{"award-number":["Qiankehejichu-ZK[2024] General 424"]}]},{"name":"Academic New Seedling Foundation Project of Guizhou Normal University","award":["Qianshixinmiao-[2021]A30"],"award-info":[{"award-number":["Qianshixinmiao-[2021]A30"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In the digital twin system of mechatronics engineering, the scale and accuracy of models are continually improving. Nevertheless, this growth can hinder real-time interaction and decision-making accuracy within digital twins. The resulting delay impacts the entire system\u2019s reliability by reducing its response speed and real-time decision-making. Consequently, there is an imperative demand for a lightweight approach to tackle the challenges arising from the escalating scale of digital twin virtual entity models. This paper presents a digital twin methodology that is lightweight. The procedure comprises three primary phases: graph data modeling, graph neural network analysis, and hierarchical simplification of virtual entities. Specifically, the graph neural network method proposed in this article is used to classify the graph data of virtual entities. Then, the model is hierarchically simplified based on the classification. Finally, experiments were conducted on factory and robotic arm datasets to evaluate the proposed method. The experimental results indicate that the DTL-GNN method can reduce system redundancy while preserving the essential features of virtual entities.<\/jats:p>","DOI":"10.3390\/fi17020065","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T05:50:36Z","timestamp":1738734636000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network"],"prefix":"10.3390","volume":"17","author":[{"given":"Chengjun","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China"},{"name":"Technical Engineering Center of Manufacturing Service and Knowledge Engineering, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liguo","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China"},{"name":"Technical Engineering Center of Manufacturing Service and Knowledge Engineering, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6241-8718","authenticated-orcid":false,"given":"Yao","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China"},{"name":"Technical Engineering Center of Manufacturing Service and Knowledge Engineering, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songsong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guizhou Qunjian Precision Machinery Company, Zunyi 563099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China"},{"name":"Technical Engineering Center of Manufacturing Service and Knowledge Engineering, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.ifacol.2021.08.186","article-title":"Leveraging the Asset Administration Shell for Agent-Based Production Systems","volume":"54","author":"Ocker","year":"2021","journal-title":"FAC-PapersOnLine"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ocker, F., Vogel-Heuser, B., Schon, H., and Mieth, R. (2021, January 13). Leveraging Digital Twins for Compatibility Checks in Production Systems Engineering. Proceedings of the 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore.","DOI":"10.1109\/IEEM50564.2021.9672892"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s38312-019-0012-0","article-title":"Digital Twin Technology for More Efficiency","volume":"6","author":"August","year":"2019","journal-title":"ATZproduction Worldw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1038\/d41586-019-02849-1","article-title":"Make More Digital Twins","volume":"573","author":"Tao","year":"2019","journal-title":"Nature"},{"key":"ref_5","first-page":"1","article-title":"Digital Twin and Its Potential Application Exploration","volume":"24","author":"Fei","year":"2018","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kahlen, F.-J., Flumerfelt, S., and Alves, A. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Transdisciplinary Perspectives on Complex Systems, Springer International Publishing.","DOI":"10.1007\/978-3-319-38756-7"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.promfg.2017.07.198","article-title":"A Review of the Roles of Digital Twin in CPS-Based Production Systems","volume":"11","author":"Negri","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bot\u00edn-Sanabria, D.M., Mihaita, A.-S., Peimbert-Garc\u00eda, R.E., Ram\u00edrez-Moreno, M.A., Ram\u00edrez-Mendoza, R.A., and Lozoya-Santos, J.d.J. (2022). Digital Twin Technology Challenges and Applications: A Comprehensive Review. Remote Sens., 14.","DOI":"10.3390\/rs14061335"},{"key":"ref_9","first-page":"1","article-title":"Five-dimension Digital Twin Model and its Ten Applications","volume":"25","author":"Fei","year":"2019","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_10","unstructured":"Kim, K.-Y., Monplaisir, L., and Rickli, J. (2023). Manufacturing Process Optimization via Digital Twins: Definitions and Limitations. Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus, Springer International Publishing. Lecture Notes in Mechanical Engineering."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pires, F., Cachada, A., Barbosa, J., Moreira, A.P., and Leitao, P. (2019, January 22\u201325). Digital Twin in Industry 4.0: Technologies, Applications and Challenges. Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland.","DOI":"10.1109\/INDIN41052.2019.8972134"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TII.2018.2873186","article-title":"Digital Twin in Industry: State-of-the-Art","volume":"15","author":"Tao","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_13","unstructured":"Chryssolouris, G. (2006). Manufacturing Systems: Theory and Practice, Springer. [2nd ed.]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/JRFID.2022.3212169","article-title":"Application of Lightweight Digital Twin System in Intelligent Transportation","volume":"6","author":"Liu","year":"2022","journal-title":"IEEE J. Radio. Freq. Identif."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sottet, J.-S., Brimont, P., Feltus, C., Gateau, B., and Merche, J.-F. (2022). Towards a Lightweight Model-Driven Smart-City Digital Twin. Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, Online, 6\u20138 February 2022, SCITEPRESS\u2014Science and Technology Publications.","DOI":"10.5220\/0010906100003119"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1007\/s10462-020-09816-7","article-title":"A Comprehensive Survey on Model Compression and Acceleration","volume":"53","author":"Choudhary","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_17","unstructured":"Bulva, J., and Szendiuch, I. (2005, January 2\u20134). Comparing of 2D and 3D Modeling of MSM. Proceedings of the Conference on Electron Devices, Tarragona, Spain."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"417","DOI":"10.3233\/ICA-200641","article-title":"3D Mesh Simplification with Feature Preservation Based on Whale Optimization Algorithm and Differential Evolution","volume":"27","author":"Liang","year":"2020","journal-title":"Integr. Comput. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1080\/0951192X.2014.880811","article-title":"Three-Dimensional (3D) CAD Model Lightweight Scheme for Large-Scale Assembly and Simulation","volume":"28","author":"Liu","year":"2015","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103649","DOI":"10.1016\/j.cad.2023.103649","article-title":"Reconstruction and Preservation of Feature Curves in 3D Point Cloud Processing","volume":"167","author":"Fugacci","year":"2024","journal-title":"Comput. Aided Des."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.jmsy.2021.12.008","article-title":"Establishing a Reliable Mechanism Model of the Digital Twin Machining System: An Adaptive Evaluation Network Approach","volume":"62","author":"Liu","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106411","DOI":"10.1016\/j.neunet.2024.106411","article-title":"Compressing Neural Networks via Formal Methods","volume":"178","author":"Ressi","year":"2024","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1109\/JSAC.2023.3310063","article-title":"Digital Twin Driven Service Self-Healing with Graph Neural Networks in 6G Edge Networks","volume":"41","author":"Yu","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105578","DOI":"10.1016\/j.autcon.2024.105578","article-title":"Advancements in Digital Twin Modeling for Underground Spaces and Lightweight Geometric Modeling Technologies","volume":"165","author":"Gong","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"064502","DOI":"10.1115\/1.4053606","article-title":"Building a Lightweight Digital Twin of a Crane Boom for Structural Safety Monitoring Based on a Multifidelity Surrogate Model","volume":"144","author":"Lai","year":"2022","journal-title":"J. Mech. Des."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lian, S., Zhang, H., Sun, W., and Zhang, Y. (2022, January 19\u201322). Lightweight Digital Twin and Federated Learning with Distributed Incentive in Air-Ground 6G Networks. Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland.","DOI":"10.1109\/VTC2022-Spring54318.2022.9860796"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17231","DOI":"10.1109\/JIOT.2023.3273402","article-title":"A Digital-Twin-Empowered Lightweight Model-Sharing Scheme for Multirobot Systems","volume":"10","author":"Xiong","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fang, L., Liu, Q., and Zhang, D. (2021). A Digital Twin-Oriented Lightweight Approach for 3D Assemblies. Machines, 9.","DOI":"10.3390\/machines9100231"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, K., Tian, J., Han, J., and Yuan, Q. (2022, January 15). A Lightweight Model-Driven MES Simulation Framework Based On Probabilistic Finite Automata. Proceedings of the 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China.","DOI":"10.1109\/ICSP54964.2022.9778640"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/0010-4485(88)90050-4","article-title":"Graph-Based Heuristics for Recognition of Machined Features from a 3D Solid Model","volume":"20","author":"Joshi","year":"1988","journal-title":"Comput. Aided Des."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/jgt.20597","article-title":"From the Coxeter Graph to the Klein Graph: From the Coxeter Graph to The Klein Graph","volume":"70","author":"Dejter","year":"2012","journal-title":"J. Graph. Theory"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dorogovtsev, S.N., and Mendes, J.F.F. (2022). The Nature of Complex Networks, Oxford University Press.","DOI":"10.1093\/oso\/9780199695119.001.0001"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.physrep.2012.01.007","article-title":"Physical Approach to Complex Systems","volume":"515","year":"2012","journal-title":"Phys. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1007\/s11431-018-9319-4","article-title":"Selecting Pinning Nodes to Control Complex Networked Systems","volume":"61","author":"Cheng","year":"2018","journal-title":"Sci. China Technol. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and Evaluating Community Structure in Networks","volume":"69","author":"Newman","year":"2003","journal-title":"Phys. Rev. E"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1140\/epjb\/s10051-023-00612-0","article-title":"Critical Phenomena in Complex Networks: From Scale-Free to Random Networks","volume":"96","author":"Nesterov","year":"2023","journal-title":"Eur. Phys. J. B"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2020WR027929","DOI":"10.1029\/2020WR027929","article-title":"Using Complex Network Analysis for Optimization of Water Distribution Networks","volume":"56","author":"Sitzenfrei","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1007\/s11071-020-05867-1","article-title":"Epidemic Dynamics of Influenza-like Diseases Spreading in Complex Networks","volume":"101","author":"Wang","year":"2020","journal-title":"Nonlinear Dyn."},{"key":"ref_39","unstructured":"Hamilton, W.L., Ying, Z., and Leskovec, J. (2017, January 4\u20139). Inductive Representation Learning on Large Graphs. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Feng, F., Huang, W., He, X., Xin, X., Wang, Q., and Chua, T.-S. (2021, January 11). Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, Canada.","DOI":"10.1145\/3404835.3462971"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bamunuarachchi, D., Georgakopoulos, D., Banerjee, A., and Jayaraman, P.P. (2021). Digital Twins Supporting Efficient Digital Industrial Transformation. Sensors, 21.","DOI":"10.3390\/s21206829"},{"key":"ref_42","unstructured":"Schroeder, W.J. (1997, January 24). A Topology Modifying Progressive Decimation Algorithm. Proceedings of the IEEE Conference on Visualization, Phoenix, AZ, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Garland, M., and Heckbert, P.S. (1997). Surface Simplification Using Quadric Error Metrics. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques\u2014SIGGRAPH \u201997, ACM Press.","DOI":"10.1145\/258734.258849"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10010-019-00382-1","article-title":"Classification and Examples of next Generation Machine Elements","volume":"84","author":"Gwosch","year":"2020","journal-title":"Forsch Ingenieurwes"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Abramovici, M., and Stark, R. (2013). Mechatronic Machine Elements: On Their Relevance in Cyber-Physical Systems. Smart Product Engineering, Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-642-30817-8"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fensel, D., \u015eim\u015fek, U., Angele, K., Huaman, E., K\u00e4rle, E., Panasiuk, O., Toma, I., Umbrich, J., and Wahler, A. (2020). How to Use a Knowledge Graph. Knowledge Graphs, Springer International Publishing.","DOI":"10.1007\/978-3-030-37439-6"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., and Zhang, W. (2014, January 24). Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623623"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"55537","DOI":"10.1109\/ACCESS.2021.3070395","article-title":"Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review","volume":"9","author":"Buchgeher","year":"2021","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102222","DOI":"10.1016\/j.rcim.2021.102222","article-title":"An Automatic Method for Constructing Machining Process Knowledge Base from Knowledge Graph","volume":"73","author":"Guo","year":"2022","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ifacol.2022.09.361","article-title":"Knowledge Graphs in Digital Twins for Manufacturing\u2014Lessons Learned from an Industrial Case at Atlas Copco Airpower","volume":"55","author":"Meyers","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_51","first-page":"217","article-title":"Exact and Inexact Graph Matching: Methodology and Applications","volume":"Volume 40","author":"Aggarwal","year":"2010","journal-title":"Managing and Mining Graph Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1450001","DOI":"10.1142\/S0218001414500013","article-title":"Graph Matching and Learning in Pattern Recognition on the Last 10 Years","volume":"28","author":"Foggia","year":"2014","journal-title":"Int. J. Patt. Recogn. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s41019-016-0029-6","article-title":"Graph-Based RDF Data Management","volume":"2","author":"Zou","year":"2017","journal-title":"Data Sci. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bi, F., Chang, L., Lin, X., Qin, L., and Zhang, W. (2016, January 14). Efficient Subgraph Matching by Postponing Cartesian Products. Proceedings of the 2016 International Conference on Management of Data, San Francisco, CA, USA.","DOI":"10.1145\/2882903.2915236"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Han, M., Kim, H., Gu, G., Park, K., and Han, W.-S. (2019, January 25). Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together. Proceedings of the 2019 International Conference on Management of Data, Amsterdam, The Netherlands.","DOI":"10.1145\/3299869.3319880"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s00778-022-00749-x","article-title":"Fast Subgraph Query Processing and Subgraph Matching via Static and Dynamic Equivalences","volume":"32","author":"Kim","year":"2023","journal-title":"VLDB J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.patcog.2014.05.018","article-title":"Efficient Subgraph Matching Using Topological Node Feature Constraints","volume":"48","author":"Dahm","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.fss.2019.02.021","article-title":"An Approach for Approximate Subgraph Matching in Fuzzy RDF Graph","volume":"376","author":"Li","year":"2019","journal-title":"Fuzzy Sets Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"176","DOI":"10.14778\/3425879.3425888","article-title":"RapidMatch: A Holistic Approach to Subgraph Query Processing","volume":"14","author":"Sun","year":"2020","journal-title":"Proc. VLDB Endow."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bai, Y., Ding, H., Bian, S., Chen, T., Sun, Y., and Wang, W. (2019, January 30). SimGNN: A Neural Network Approach to Fast Graph Similarity Computation. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, Australia.","DOI":"10.1145\/3289600.3290967"},{"key":"ref_61","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_62","unstructured":"Kipf, T.N., and Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the International Conference on Learning Representations. arXiv."},{"key":"ref_63","unstructured":"Brody, S., Alon, U., and Yahav, E. (2021). How Attentive Are Graph Attention Networks?. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Song, Y., and Wang, D. (2022, January 14\u201318). Learning on Graphs with Out-of-Distribution Nodes. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/3534678.3539457"},{"key":"ref_65","unstructured":"Wang, X., Liu, H., Shi, C., and Yang, C. (2021, January 6\u201314). Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration. Proceedings of the NIPS\u201921: 35th International Conference on Neural Information Processing Systems, Online."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/2\/65\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:27:09Z","timestamp":1760027229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/2\/65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,5]]},"references-count":65,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["fi17020065"],"URL":"https:\/\/doi.org\/10.3390\/fi17020065","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,5]]}}}