Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 19 of 19 for “"temporal graph"”.
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Dynamic Spatio-Temporal Graph Convolutional Networks
Spatio-temporal modeling is an essential lens to understand many real-world phenomena from traffic [20] [10] to epidemiology [12]. Although forecasting time series is an exceptionally well-studied problem, recent years have seen impressive gains in the performance of graph learning as a paradigm …
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Temporal Graph Record Linkage and k-Safe Approximate Match
… ALIM – Aggregate Link and Iterative Match is a graph-based record linkage methodology that uses a multi-graph to store demographic data about people. ALIM performs string matching as records are inserted into the graph. ALIM eliminates data redundancy and stores the relationships between data. …
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A framework for programming and optimizing temporal graph neural networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that …
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MACHINE LEARNING FOR TEMPORAL HETEROGENEOUS GRAPHS: PREDICTIVE METHODS, INTERPRETABILITY AND APPLICATIONS.
… and interpreting predictive methods for temporal heterogeneous graphs by integrating modeling and tools from network science and graph deep learning. We introduce discrete-time graph learning architectures based on Graph Neural Networks (GNNs) and linear scoring functions tailored for …
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Context-aware pedestrian intent prediction for connected and automated vehicles
… deep learning. Research in deep learning on graphs is gaining momentum, showcasing the powerful descriptive capabilities of graph structures. These structures provide essential relationship data among various elements, proving invaluable across diverse learning applications. Our study …
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Systems and Techniques for Efficient Real-World Graph Analytics
Graphs are a natural way to model real-world entities and relationships between them, ranging from social networks and biological datasets to cloud computing infrastructure data lineage graphs. Queries over these large graphs often involve expensive subgraph traversals and complex analytical …
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Link Prediction on Distributed Systems
… these interactions, prompting the use of dynamic graph-based models, such as Graph Neural Networks (GNNs) and transformer-based architectures, for link prediction tasks. Despite their success, these models struggle with large-scale, temporal data and limited generalization capabilities. This …
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Biomechanical Validation of Skeletal Tracking Data and Developing Action Recognition Models for Basketball: A Baseline for NBA Officiating Tools
… models—a transformer-based model and a temporal graph neural network—are implemented to classify player actions, specifically dribbling, passing, shooting, and rebounding, from sequences of skeletal tracking frames. The objective is to establish a baseline for developing tools to support …
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Novel Machine Learning Models Based Uncertainty Estimation and Sequential Predictions for Blockchain Networks
… insights from large-scale blockchain data via graph network analysis and visual analytics tools. However, a straightforward visualisation is not very effective with the increasing complexity of the blockchain network. On the other hand, a machine learning approach is capable of dealing with the …
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Multimedia Big Data Analytics and Fusion for Data Science
… project workflow, including data fusion, spatio-temporal deep feature extraction, and model training optimization strategy. First, a hierarchical graph fusion network is presented to capture the inter-modality correlations among modalities. The network hierarchy models the modality-wise …
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Temporal Reasoning in Clinical Narratives: From Information Extraction to Early Disease Detection
… This thesis presents a unified framework for temporally grounded patient modeling that integrates structured event representations, fine-grained temporal reasoning, and scalable predictive architectures. Initial experiments show that concept-based models paired with visit-level temporal …
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Learning Spatio-Temporal Correlations from Dynamic and Sparse Data
The amount of spatio-temporal measurements around Earth, its atmosphere, in human-made urban settings, and in its oceans increases with affordable and smaller sensor technologies. Improved communication technologies allow sensors to be not geostationary, changing their positions and measurement …
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Spatiotemporal Event Graphs for Dynamic Scene Understanding
… the first method, we present a deformable, spatiotemporal scene graph approach, consisting of three main building blocks: action tube detection, a 3D deformable RoI pooling layer designed for learning the flexible, deformable geometry of the constituent action tubes, and a scene graph constructed …
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Information extraction from digital social trace data with applications to social media and scholarly communication data
… This identification allows us to utilize the graph structure of the data (e.g., user connected to a tweet, author connected to a paper, author connected to authors, etc.) for developing new information extraction tasks. The thesis focuses on information extraction from DSTD, first, using only …
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Machine Learning Driven Source Identification, State Estimation and Sensor Optimization in Water Systems
… presents one of the first attempts to apply Graph Neural Networks (GNN) to estimate water quality parameters at unmonitored junctions. This was achieved by developing two GNN models. The first is a Static Prediction GNN (SP-GNN) model, which provides accurate state estimation for fixed sensor …
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Semantic and spatio-temporal understanding for computer vision driven worker safety inspection and risk analysis
… methods for automated semantic and spatio-temporal visual understanding of workers and equipment and how to use them to improve automatic safety inspections and risk analysis: (1) a new method is developed to improve the breadth and depth of vision-based safety compliance checking by …
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Advanced Methods for Entity Linking in the Life Sciences
… Nevertheless, only a few methods can handle graph-structured knowledge bases or consider temporal aspects. The temporal aspects are essential to identify the same entities over different time intervals since these aspects underlie certain conditions. Moreover, records can be related to other …
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Towards A Robust Integrated Urban Mobility System: Public Transit and Ride-Sharing Systems
… a strategy utilizing a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN), aimed at improving demand forecast accuracy while reducing discrepancies in prediction errors across diverse regions. For equitable repositioning of the supply side vehicles, we introduce a …