A study on graph neural networks
This report investigates various Graph Neural Network (GNN) models and its performance and stability. GNNs have gained popularity in recent years because they are able to handle graph data structures, which are a common way to represent complex relationships between entities in many real-world appli...
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| Materialtyp: | Final Year Project (FYP) |
| Språk: | English |
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Nanyang Technological University
2023
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| Länkar: | https://hdl.handle.net/10356/167731 |
| _version_ | 1826117933268467712 |
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| author | Choo, Patricia Yu Wei |
| author2 | Tay Wee Peng |
| author_facet | Tay Wee Peng Choo, Patricia Yu Wei |
| author_sort | Choo, Patricia Yu Wei |
| collection | NTU |
| description | This report investigates various Graph Neural Network (GNN) models and its performance and stability. GNNs have gained popularity in recent years because they are able to handle graph data structures, which are a common way to represent complex relationships between entities in many real-world applications. This project focuses on node classification problems and is tested on public benchmark datasets. The paper discusses the possible improvement in performance and stability using Bootstrapped Graph Latents (BGRL) and compare it to bootstrapping neural networks. |
| first_indexed | 2024-10-01T04:35:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167731 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:35:28Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1677312023-07-07T19:35:06Z A study on graph neural networks Choo, Patricia Yu Wei Tay Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Engineering::Computer science and engineering Engineering::Electrical and electronic engineering This report investigates various Graph Neural Network (GNN) models and its performance and stability. GNNs have gained popularity in recent years because they are able to handle graph data structures, which are a common way to represent complex relationships between entities in many real-world applications. This project focuses on node classification problems and is tested on public benchmark datasets. The paper discusses the possible improvement in performance and stability using Bootstrapped Graph Latents (BGRL) and compare it to bootstrapping neural networks. Bachelor of Engineering (Information Engineering and Media) 2023-06-03T13:36:58Z 2023-06-03T13:36:58Z 2023 Final Year Project (FYP) Choo, P. Y. W. (2023). A study on graph neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167731 https://hdl.handle.net/10356/167731 en A3017-221 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Engineering::Electrical and electronic engineering Choo, Patricia Yu Wei A study on graph neural networks |
| title | A study on graph neural networks |
| title_full | A study on graph neural networks |
| title_fullStr | A study on graph neural networks |
| title_full_unstemmed | A study on graph neural networks |
| title_short | A study on graph neural networks |
| title_sort | study on graph neural networks |
| topic | Engineering::Computer science and engineering Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167731 |
| work_keys_str_mv | AT choopatriciayuwei astudyongraphneuralnetworks AT choopatriciayuwei studyongraphneuralnetworks |