Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/238620
DC Field | Value | |
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dc.title | TOWARD EFFECTIVE NEURAL NETWORKS ON GRAPH DATA | |
dc.contributor.author | WANG YIWEI | |
dc.date.accessioned | 2023-03-31T18:00:31Z | |
dc.date.available | 2023-03-31T18:00:31Z | |
dc.date.issued | 2022-07-19 | |
dc.identifier.citation | WANG YIWEI (2022-07-19). TOWARD EFFECTIVE NEURAL NETWORKS ON GRAPH DATA. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/238620 | |
dc.description.abstract | Graphs are ubiquitous in many practical settings, e.g., social networks, financial systems, transportation planning, online shopping recommendation, etc. Graph Neural Networks (GNNs) are a promising approach for learning from graph data, but they have yet to reach the successes of deep learning in natural language processing and computer vision. In this thesis, we explore to enhance the performance of neural networks for graph learning. First, by using Data Augmentation (DA), we present a new method to enhance Graph Convolutional Networks (GCNs). Second, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification. we present the MeTA (Memory Tower Augmentation) module: a multi-level module that processes the augmented graphs of different magnitudes on separate levels, and performs message passing across levels to provide adaptively augmented inputs for every prediction. Last but not least, we present a new neighbor sampling method on temporal graphs. | |
dc.language.iso | en | |
dc.subject | graph neural networks, data augmentation, link prediction, temporal graph, consistency training, node classification | |
dc.type | Thesis | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.supervisor | Kuen-Yew Bryan Hooi | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY (SOC) | |
Appears in Collections: | Ph.D Theses (Open) |
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WangYW.pdf | 4.1 MB | Adobe PDF | OPEN | None | View/Download |
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