Dissertation/ Thesis
Robust and efficient spatio-temporal graph learning
| Title: | Robust and efficient spatio-temporal graph learning |
|---|---|
| Authors: | Zhang, Qianru, 張倩茹 |
| Contributors: | Yiu, SM |
| Publisher Information: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Publication Year: | 2024 |
| Collection: | University of Hong Kong: HKU Scholars Hub |
| Subject Terms: | Geospatial data - Computer processing |
| Description: | The rapid growth of urban data has sparked significant interest in the field of urban studies. As cities consist of diverse regions such as business districts, residential areas, and more, generating high-quality region spatial-temporal graph embeddings is crucial for understanding the underlying structures of urban environments. These embeddings have the potential to contribute to the development of smarter and more sustainable cities. Additionally, they directly impact various downstream prediction tasks, including traffic flow prediction, crime prediction, and others. The proliferation of mobile computing technologies has resulted in an unprecedented availability of urban data, such as taxi trajectories and Point-of-Interests (POIs), providing valuable support for exploring and analyzing region embeddings. However, generating high-quality region embeddings from abundant city data presents challenges, such as data sparsity. In this thesis, we propose three novel frameworks for robust and efficient spatio-temporal graph learning. Our approach utilizes graph neural networks (GNNs) to capture spatial dependencies and temporal dynamics in the graph data. We incorporate robust regularization techniques to enhance the model's resilience to noise and outliers. Additionally, we introduce two new frameworks for anomalous subtrajectory detection and traffic prediction. Through extensive experiments on real-world spatial-temporal datasets, we demonstrate the superior robustness and efficiency of our proposed framework compared to state-of-the-art methods. Our approach not only achieves accurate predictions but also ensures the model's stability and reliability, even in the presence of noisy or outlier data points. Furthermore, our efficient graph sampling strategy enables faster training and inference, making our framework applicable to large-scale spatio-temporal graph datasets. The robustness and efficiency of our proposed framework make it well-suited for various applications, including urban planning, transportation ... |
| Document Type: | doctoral or postdoctoral thesis |
| Language: | English |
| Relation: | HKU Theses Online (HKUTO); 991044829503603414; https://hub.hku.hk/handle/10722/344159 |
| Availability: | https://hub.hku.hk/handle/10722/344159 |
| Rights: | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Accession Number: | edsbas.77FDA884 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hub.hku.hk/handle/10722/344159# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.77FDA884 RelevancyScore: 872 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 871.7080078125 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Robust and efficient spatio-temporal graph learning – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Qianru%22">Zhang, Qianru</searchLink><br /><searchLink fieldCode="AR" term="%22張倩茹%22">張倩茹</searchLink> – Name: Author Label: Contributors Group: Au Data: Yiu, SM – Name: Publisher Label: Publisher Information Group: PubInfo Data: The University of Hong Kong (Pokfulam, Hong Kong) – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Hong Kong: HKU Scholars Hub – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Geospatial+data+-+Computer+processing%22">Geospatial data - Computer processing</searchLink> – Name: Abstract Label: Description Group: Ab Data: The rapid growth of urban data has sparked significant interest in the field of urban studies. As cities consist of diverse regions such as business districts, residential areas, and more, generating high-quality region spatial-temporal graph embeddings is crucial for understanding the underlying structures of urban environments. These embeddings have the potential to contribute to the development of smarter and more sustainable cities. Additionally, they directly impact various downstream prediction tasks, including traffic flow prediction, crime prediction, and others. The proliferation of mobile computing technologies has resulted in an unprecedented availability of urban data, such as taxi trajectories and Point-of-Interests (POIs), providing valuable support for exploring and analyzing region embeddings. However, generating high-quality region embeddings from abundant city data presents challenges, such as data sparsity. In this thesis, we propose three novel frameworks for robust and efficient spatio-temporal graph learning. Our approach utilizes graph neural networks (GNNs) to capture spatial dependencies and temporal dynamics in the graph data. We incorporate robust regularization techniques to enhance the model's resilience to noise and outliers. Additionally, we introduce two new frameworks for anomalous subtrajectory detection and traffic prediction. Through extensive experiments on real-world spatial-temporal datasets, we demonstrate the superior robustness and efficiency of our proposed framework compared to state-of-the-art methods. Our approach not only achieves accurate predictions but also ensures the model's stability and reliability, even in the presence of noisy or outlier data points. Furthermore, our efficient graph sampling strategy enables faster training and inference, making our framework applicable to large-scale spatio-temporal graph datasets. The robustness and efficiency of our proposed framework make it well-suited for various applications, including urban planning, transportation ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: HKU Theses Online (HKUTO); 991044829503603414; https://hub.hku.hk/handle/10722/344159 – Name: URL Label: Availability Group: URL Data: https://hub.hku.hk/handle/10722/344159 – Name: Copyright Label: Rights Group: Cpyrght Data: The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. – Name: AN Label: Accession Number Group: ID Data: edsbas.77FDA884 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.77FDA884 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Geospatial data - Computer processing Type: general Titles: – TitleFull: Robust and efficient spatio-temporal graph learning Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Qianru – PersonEntity: Name: NameFull: 張倩茹 – PersonEntity: Name: NameFull: Yiu, SM IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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