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 18 of 18 for “"Graph machine learning."”.
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Medical Image Analysis Based on Graph Machine Learning and Variational Methods
… imaging. We introduce a novel approach utilizing Graph Neural Networks (GNNs) that incorporate both spectral and spatial insights for segmentation. By leveraging various supervoxel creation methods such as VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb-Huttenlocher, we structured 3D MRI images …
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Optimizing Out-Of-Memory Sparse-Dense Matrix Multiplication
… (SpMDM), with a focused application on graph machine learning workloads, such as graph neural networks (GNNs), though this work is general enough such that it should apply to any application tailored for running matrix multiplication workloads that cannot fit in memory. Specifically, we …
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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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A Multimodal Graph Convolutional Approach to Predict Genes Associated with Rare Genetic Diseases
… and genes, and develop an approach based on graph convolutional networks. We show how our model design considerations impact prediction performance. We demonstrate that our approach outperforms simpler graph machine learning and traditional machine learning approaches, as well as a …
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Data-centric methods for optimization and pattern discovery in networked systems
… on identifying design patterns in architecture graph representations of operational systems. Design patterns have been well documented and researched in software systems as a valuable design tool since the nineties. However, their usage has not been significantly expanded beyond software …
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Non-parametric modelling of signals on graphs
Graphs are simple yet powerful data structures that describe entities and their relationships between each other using nodes and edges, making them popular candidates for modelling a wide variety of real-world objects, ranging from molecules to social or biological networks. As a result of their …
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ScaleGPS: Scalable Graph Parallel Sampling via Data-centric Performance Engineering
Graph sampling extracts representative samples of a graph, so that approximate graph algorithms can be used in place of expensive, exact algorithms while still achieving highquality results. Thus, graph sampling plays an important role in many modern graph-based applications, such as graph machine …
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Discovering Heterogeneous Causal Effects in Relational Data
… Causal Model (NSCM) and Network Abstract Ground Graph (NAGG), a framework for expressive causal modeling and sound causal reasoning in networks, along with IDE-Net, an approach for robust individual direct effect estimation when underlying mechanisms of heterogeneous peer influence (HPI) are …
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Data-driven and Machine Learning approaches for exploration and inference of Biological Pathways in physiological and pathological states
… the epigenetic changes in cancer through machine learning models that classify cancer types and subtypes. Interpretation of these models through pathway analysis techniques confirms that the genomic loci they detect are involved in cancer processes. Despite the central importance and broad …
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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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Solving Graph Problems with Large Language Models
… to solve classical computational problems on graphs. Graphs are a fundamental abstraction for representing real-world systems, such as social, transportation, and communication networks, but they pose unique challenges: their structure is not tied to any fixed ordering of nodes (graph …
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Detect, Explain, Ground: A Sustainable Pipeline for Robust Large Language Model–Powered Agents
… breakdowns, we introduce a Hierarchical Lexical Graph to improve evidence retrieval for multi-hop question answering. Across five datasets, this graph-augmented retrieval method achieves a 23.1% relative improvement in recall and correctness over baseline RAG systems. Together, these …
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Robust graph representation learning with structure-aware attention and self-supervised contrastive frameworks.
Graph-structured data is pervasive across domains such as social networks, biological systems, and information networks, yet effectively learning from such data remains a fundamental challenge in machine learning. My dissertation focuses on developing novel graph representation learning methods …
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Empowering graph intelligence via natural and artificial dynamics
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Design and Evaluation of Network Algorithms and Deep Learning Models in Systems Biology and Biomedicine
… present ICoN, an unsupervised coattention-based graph neural network model for integrating heterogeneous protein-protein interaction networks. ICoN learns joint embeddings across multiple networks and captures complementary biological evidence. ICoN surpassed individual networks across three …
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Supervised Inference of Gene Regulatory Networks
… interactions by design. To facilitate supervised learning, we propose a novel graph convolutional neural network (GCN) based autoencoder to infer new regulatory edges from a known GRN and scRNA-seq data. As the name suggests, a GCN-based autoencoder consists of an encoder that learns a …
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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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Effective knowledge extraction and knowledge-enhanced machine learning for health
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms