Learning a mixture of graph neural networks
GNN models are designed to handle complex and non-uniform graph-structured data for classification tasks such as node, link, and graph-level predictions. However, the randomness and unfixed node ordering of graph data still proposes a challenge to GNN models. To improve the accuracy of GNN mod...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167150 |