Deep graph neural networks for link prediction
Graph neural networks (GNNs) is a form of machine learning architecture that uses many neurons to learn a given information which is similar to how a human brain works. It is also known as deep GNNs when there are many layers of information processing within the neural network architecture. GNNs...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/177145 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Graph neural networks (GNNs) is a form of machine learning architecture that uses
many neurons to learn a given information which is similar to how a human brain
works. It is also known as deep GNNs when there are many layers of information
processing within the neural network architecture. GNNs can be used for many machine
learning tasks and can be used for learning networks such as citation networks. In this
project, the main focus will be the investigation of inference performance of GNN
models for link prediction task. Research in GNNs in the recent years has been agile but
there is not enough experiments and discussions on the different hyperparameters and
architectures that are being implemented. A literature review of the different GNN
models and architectures was conducted. Comparisons between using different
hyperparameters and architectures will be conducted for analyzing and discussing the
strengths and weaknesses of the different configurations and frameworks. Upon
investigations of the results, it was determined that the different datasets, model
parameters and hyperparameters affects the inference performance differently for GNN
models. |
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