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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Main Author: Zheng, MingXi
Other Authors: Tay Wee Peng
Format: Final Year Project
Language:English
Published: 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
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spelling sg-ntu-dr.10356-1771452024-05-31T15:43:13Z Deep graph neural networks for link prediction Zheng, MingXi Tay Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Computer and Information Science 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. Bachelor's degree 2024-05-27T06:20:04Z 2024-05-27T06:20:04Z 2024 Final Year Project (FYP) Zheng, M. (2024). Deep graph neural networks for link prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177145 https://hdl.handle.net/10356/177145 en A3203-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle Computer and Information Science
Zheng, MingXi
Deep graph neural networks for link prediction
description 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.
author2 Tay Wee Peng
author_facet Tay Wee Peng
Zheng, MingXi
format Final Year Project
author Zheng, MingXi
author_sort Zheng, MingXi
title Deep graph neural networks for link prediction
title_short Deep graph neural networks for link prediction
title_full Deep graph neural networks for link prediction
title_fullStr Deep graph neural networks for link prediction
title_full_unstemmed Deep graph neural networks for link prediction
title_sort deep graph neural networks for link prediction
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177145
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