Graph neural differential equation networks for improved representation learning and robustness
Graph representation learning distills the complex structures of graphs into tractable, low-dimensional vector spaces, capturing essential topological and attribute-based properties. Graph Neural Networks (GNNs) have become a pivotal tool in this domain, leveraging graph structures to iteratively up...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2025
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| Online Access: | https://hdl.handle.net/10356/182340 |