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 20 of 25 for “"graph learning"”.
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Optimal graph learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Deep graph learning for social-info dynamics
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Graph Learning and Optimization for Irregular-Structured Signal Processing
Graph Signal Processing (GSP) extends harmonic analysis tools, such as Fourier transforms and wavelets, to discrete signals defined on finite graphs, enabling tasks like signal denoising, prediction, and interpolation on irregular domains. A critical first step in GSP is to learn an appropriate …
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Fast Partitioning for Distributed Graph Learning using Multi-level Label Propagation
Graph Neural Networks (GNNs) are a popular class of machine learning models that allow scientists to leverage machine learning techniques to perform inference on unstructured data. However, when graphs become too large, partitioning becomes necessary to allow for distributed computation. Standard …
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Integrating Feature and Graph Learning with Factorization Models for Low-Rank Data Representation
… such as computer vision, machine learning, and data mining. High-dimensional data usually have intrinsic low-dimensional structures, which are suitable for subsequent data processing. As a consequent, it has been a common demand to find low-dimensional data representations in many …
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MACHINE LEARNING FOR TEMPORAL HETEROGENEOUS GRAPHS: PREDICTIVE METHODS, INTERPRETABILITY AND APPLICATIONS.
… 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 forecasting dynamic, …
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Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal processing (GSP) tasks, such as graph signal compression and denoising. Previous graph learning algorithms either make assumptions on graph connectivity (e.g., graph sparsity), or make individual edge weight assumptions such …
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Machine learning for biological networks
… study. On the other hand, the advance of graph learning algorithms has made it possible to build data-driven models for large graph problems. These methods generally fall into two categories: 1) random walk and 2) deep graph neural net. We study how to leverage information from biological …
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Graph feature engineering and coordinate-based learning for transferable and energy-efficient artificial intelligence
… framework for efficient and scalable graph representation learning is presented, emphasizing coordinate-based and explicit structural methods. The research addresses the limitations of Graph Neural Networks (GNNs) in resource-constrained environments, including edge devices and …
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Dynamic Spatio-Temporal Graph Convolutional Networks
… have seen impressive gains in the performance of graph learning as a paradigm for spatial learning problems. Some recent work has explored the intersection of these two fields but often assumes that the underlying graph structure is static. We introduce Dynamic Spatio-Temporal Graph Convolution …
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Attention-based representation learning on graphs
… readily available, the field of representation learning has continued to evolve through approaches that seek to describe, understand, and even unify deep learning strategies for data structures such as sets, grids, and graphs. A remarkably successful application of this field of geometric deep …
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MPrompt: A Pretraining-Prompting Scheme for Enhanced Fewshot Subgraph Classification
… by the significant progress in NLP prompt learning, there have been great research interests recently in adopting the prompting mechanism for graph machine learning. Despite the prior success of prompting methods applied in node-level and graph-level learning tasks, subgraph-level tasks are …
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Hidden Influence in Dynamic Networks
… I begin with a foundational overview of graph learning techniques and the specific models utilized in my work. The body of this dissertation is divided into three core sections. The first examines the orchestration of influence campaigns by state-backed entities on social media, utilizing …
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Learning from Structured Data with Weak Supervision
… over the past decade include self-supervised learning methods that train models on broad data at scale without pre-defined labels, geometric deep learning that leverages structure and geometry informed by scientific knowledge, and generative AI methods that create action plans for experiments …
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Graph-Based Computational Approaches for Modeling Viral Evolution
… relationships are more naturally captured by graphs than trees. In this dissertation, I develop a sequence of graph-centered frameworks that integrate viral fitness, mutational distance, and mutational dynamics to model viral evolution from algorithmic and data-driven perspectives. First, …
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Learning compact hashing codes with complex objectives from multiple sources for large scale similarity search
… the missing data problem by latent subspace learning from multiple sources. The hashing codes are learned by enforcing the data consistency among different sources. Thirdly, we address the problem of hashing on structured data by graph learning. A weighted graph is constructed based on the …
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Sparse modeling of high-dimensional data for learning and vision
… of high-dimensional signals for various learning and vision tasks, including image classification, single image super-resolution, compressive sensing, and graph learning. Based on the bag-of-features (BoF) image representation in a spatial pyramid, we first transform each local image …
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Inferring Undirected and Causally Directed Graph Structures from Multivariate Time Series
… primates using a novel variation of undirected graph learning based on smoothness prior. In Part Two, we define and implement a novel spatiotemporal graph (STG) model for inferring causally directed graphs. Analysis of brain connectivity networks has a potential to advance our understanding of …
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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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Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
Recent advances in large graph-based models have shown great performance in a variety of tasks, including node classification, link prediction, and influence modeling. However, these graph-based models struggle to capture high-order relations and interactions among entities effectively, leading …
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