The effectiveness of GNNs for node classification : the significance of side information
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Liu, Xiaoou
issued: 2024-06-17T21:41:37Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: This thesis studies the effectiveness of Graph Neural Networks (GNNs) for node classification. We conduct systematic experiments on several representative deep-learning models for graph data, using training data generated from the Stochastic Block Model (SBM) and the theoretical results on the fundamental limits of this model as guidance in the design of our experiments. While GNNs are widely believed to be powerful learning models for graph data, our empirical findings suggest that they do not necessarily outperform other machine-learning-based methods and traditional algorithms for node classification. In particular, we observe that GNN-based methods fail to exploit the information from labeled nodes in semi-supervised learning settings. We propose an effective data augmentation method to enhance GNN-based methods by making better use of labeled information in the training data. Our experiments using synthetic data from SBMs and real-world datasets demonstrate that our method can significantly enhance the capabilities of GNN models and notably improve their performance for node classification. Additionally, in the context of unsupervised learning, we discuss the possibility of incorporating other types of side information into GNNs that may exist in multiplex network data.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88475
note: The Effectiveness of GNNs forNode Classification: TheSignificance of Side InformationbyXiaoou LiuB.Eng., Beijing Jiaotong University, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)June 2024© Xiaoou Liu 2024The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, the thesis entitled:The Effectiveness of GNNs for Node Classification: The Significance ofSide Informationsubmitted by Xiaoou Liu in partial fulfillment of the requirements for thedegree of Master of Science.Dr. Yong Gao, Irving K Barber Faculty of ScienceSupervisorDr. Shan Du, Irving K Barber Faculty of ScienceDefense Committee MemberDr. Ifeoma Adaji, Irving K Barber Faculty of ScienceDefense Committee MemberDr. Suliman Gargoum, School of EngineeringUniversity ExamineriiAbstractThis thesis studies the effectiveness of Graph Neural Networks (GNNs)for node classification. We conduct systematic experiments on several repre-sentative deep-learning models for graph data, using training data generatedfrom the Stochastic Block Model (SBM) and the theoretical results on thefundamental limits of this model as guidance in the design of our experiments.While GNNs are widely believed to be powerful learning models for graphdata, our empirical findings suggest that they do not necessarily outperformother machine-learning-based methods and traditional algorithms for nodeclassification. In particular, we observe that GNN-based methods fail to ex-ploit the information from labeled nodes in semi-supervised learning settings.We propose an effective data augmentation method to enhance GNN-basedmethods by making better use of labeled information in the training data.Our experiments using synthetic data from SBMs and real-world datasetsdemonstrate that our method can significantly enhance the capabilities ofGNN models and…
has type: Thesis/Dissertation
date issued: 2024-09
is shown at: 10.14288/1.0443981
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Gao, Yong
type: Text
identifier URI: http://hdl.handle.net/2429/88475