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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Main Author:	Choo, Patricia Yu Wei
Other Authors:	Tay Wee Peng
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2023
Subjects:	Engineering::Computer science and engineering Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/167731
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.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
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Computer science and engineering Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Computer science and engineering Engineering::Electrical and electronic engineering Choo, Patricia Yu Wei A study on graph neural networks
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.
author2	Tay Wee Peng
author_facet	Tay Wee Peng Choo, Patricia Yu Wei
format	Final Year Project
author	Choo, Patricia Yu Wei
author_sort	Choo, Patricia Yu Wei
title	A study on graph neural networks
title_short	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_sort	study on graph neural networks
publisher	Nanyang Technological University
publishDate	2023
url	https://hdl.handle.net/10356/167731
_version_	1772826494593138688

A study on graph neural networks

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