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 170 for “"Graph Neural Networks"”.
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Peer evaluation with graph neural networks
… peer assessment as multi-relational weighted networks that can represent a variety of peer assessment setups, and capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph convolutional network which can learn assessment patterns …
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Modeling Intelligence via Graph Neural Networks
… world. We study both questions from the lens of graph neural networks, a class of neural networks acting on graphs. First, we can abstract many objects in the world as graphs and learn their representations with graph neural networks. Second, we shall see how graph neural networks exploit the …
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Graph Neural Networks: Techniques and Applications
… to the geometry of the data represented by a graph. Typical applications include social networks, transportation networks, the spread of epidemic disease, brain's neuronal networks, gene data on biological regulatory networks, telecommunication networks, knowledge graph, which are lying on the …
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Demystifying graph neural networks in recommender systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms
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Belief propagation on factor graph neural networks
Probabilistic graphical models are a statistical framework for conditionally dependent random variables with dependencies represented by graphs. A traditional method to perform inference over these random variables is Belief Propagation. Belief Propagation can be used to compute an exact solution …
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Graph Neural Networks for Multi-Agent Learning
… By focusing on the underlying relationships in graph structured data, it has become possible to create models with superior performance and generalisation. Given different graph topologies, these models can manifest in the form of CNNs (on grid graphs), RNNs (on line graphs), and Transformers …
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Graph Neural Networks for Multi-Robot Coordination
… in investigating machine learning (especially graph neural network) based approaches to find the trade-off between optimality and complexity by offloading online computation into an offline training process. Yet, learning-based methods also yield the need for sim-to-real systems and solutions …
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On Counting Substructures with Graph Neural Networks
To achieve a graph representation, most Graph Neural Networks (GNNs) follow two steps: first, each graph is decomposed into a number of subgraphs (which we call the recursion step), and then the collection of subgraphs is encoded by several iterative pooling steps. While recently proposed …
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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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Practical processing and acceleration of graph neural networks
… Machine Learning (ML) in the past decade. Deep neural networks have achieved, or surpassed, human-level on diverse tasks ranging from image classification to game playing. In these applications, we typically observe that the input to the model has some form of regular structure: for example, …
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Faithful and fair generative explainers for graph neural networks
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness across various real-world applications; however, their underlying mechanisms remain a mystery. Explaining GNNs is crucial for understanding their complex underlying mechanisms, ensuring application safety, and enhancing model …
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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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Scalability and interpretability of graph neural networks for small molecules
In this thesis I examine the use of graph neural networks for prediction tasks in chemistry with an emphasis on interpretable and scalable methods. I propose a novel kernel-inspired graph neural network architecture, called a subgraph matching neural network (SMNN), which is designed to have all …
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Log file anomaly detection using knowledge graphs and graph neural networks
… proposed representing log files as knowledge graphs (KGs) and using KG completion (KGC) techniques to predict new facts. However, current research in this area is limited, and there is no end-to-end system that includes both KG generation and KGC for log-based anomaly detection. In this study, …
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Advancing domain decomposition methods and entity resolution with graph neural networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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Active heterogeneous graph neural networks with per-step meta-q-learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Inference in Ising models by graph neural networks with structural features
Probabilistic graphical models (PGMs) are powerful frameworks for modeling interactions between random variables. The two major inference tasks on PGMs are marginal probability inference and maximum-a-posteriori (MAP) inference. Exact inference on PGMs is intractable, hence approximation …
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Using heterogeneous Graph Neural Networks(hGNN) to predict cell-cell communication
… (scRNA-seq) data. We evaluate the performance of Graph Neural Networks (GNNs) both with and without gene-gene edges, Contrastive Learning, and Variational Autoencoders (VAEs) across multiple datasets. Our study compares these methods and establishes benchmarks for assessing their effectiveness …
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