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 83 for “"Graph representation"”.
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Graph Representation Learning for Social Networks
… is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings. These representations are fed into off-the-shelf machine learning algorithms to simplify and speed up graph analytic tasks. Given the immense …
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Graph Representation of Topological Stabilizer States
… While every stabilizer state maps to a graph state under local Clifford operations, the graphs associated with topological stabilizer codes remain unknown. In this thesis, I show that the toric code graph is composed of only two kinds of subgraphs: star graphs and half graphs. The …
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Graph Representation Learning for Drug Discovery
… To this end, we propose hierarchical graph generative models that make use of larger structural building blocks derived from either tree decomposition of molecular graphs or molecular rationales explaining the outcome of property predictors. Lastly, we demonstrate how these techniques …
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Graph Representation Learning to Study the Tumour Microenvironment
… of biology, this thesis focuses on using graph representation learning on HMBI to highlight unknown biological patterns in a data-driven manner. Here I propose MULTIPLAI, a novel framework to predict clinical biomarkers from HMBI data using Graph Neural Networks (GNNs) that integrate both …
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Neural graph representation learning with application to chemistry
… deep learning algorithms for learning continuous representation of molecular graphs, a much more compact representation than traditional fingerprints. We demonstrate its better predictive performance in two tasks. First, we seek to automate the prediction of organic reaction outcomes. The previous …
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Efficient Systems for Large-Scale Graph Representation Learning
Graph representation learning has gained significant traction in critical domains including finance, social networks, and transportation systems due to its successful application to graphstructured data. Graph neural networks (GNNs), which integrate the power of deep learning with graph structures, …
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Exploring the power of text-rich graph representation learning
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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Molecular Graph Representation Learning and Generation for Drug Discovery
… in the context of two molecular problems: representation and generation. Canonical success of deep learning is deeply rooted in its ability to map the input domain into a meaningful representation space. This is especially poignant for molecular problems, where the “right” relations between …
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Deep Graph Representation Learning and its Application on Graph Clustering
Graphs like social networks, molecular graphs, and traffic networks are everywhere in the real world. Deep Graph Representation Learning (DGL) is essential for most graph applications, such as Graph Classification, Link Prediction, and Community Detection. DGL has made significant progress in …
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Distributional and relational inductive biases for graph representation learning in biomedicine
… modelling efforts have focused on deep representation learning methods which offer a flexible modelling paradigm to handling high dimensional data at scale and incorporating inductive biases. The emerging field of representation learning on graph structured data opens opportunities to …
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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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Computing Equilibria in Colonel Blotto by Applying Counterfactual Regret Minimization Using a Layered Graph Representation
… by representing the action space using layered graphs. Recently, the multiplicative weights update (MWU) algorithm was implemented in Colonel Blotto by [4] that took advantage of sampling from the action space to learn in larger game settings. We take advantage of the layered graph …
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Air traffic control using Virtual Stationary Automata
… the air traffic control system as a directed graph, showing how the continuous real world air traffic can be abstracted into a discrete graph representation. Using this graph representation, I provide two algorithms to perform safe and efficient air traffic control and prove their …
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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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Graph structures, random walks, and all that : learning graphs with jumping knowledge networks
Graph representation learning aims to extract high-level features from the graph structures and node features, in order to make predictions about the nodes and the graphs. Applications include predicting chemical properties of drugs, community detection in social networks, and modeling interactions …
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Automated Metamodel Instance Generation Satisfying Quantitative Constraints
… thesis presents a solution that combines both graph representation and Satisfiability Modulo Theories (SMT) to the problem of metamodel instance generation. The solution consists of two approaches, the first approach presents a new foundation for generating metamodel instances by translating a …
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Quantum Computing from Graphs
… or coding algorithm analysis. We introduce a representation of stabilizer codes as graphs with certain structures. Specifically, the graphs take a semi-bipartite form wherein input nodes map to output nodes, such that output nodes may connect to each other but input nodes may not. Intuitively, …
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The development of a scenario independent method for evaluating the evacuation complexity of a building
… for evacuation complexity. However, these graph measures are primarily used to measure the relative accessibility of different locations in a spatial system and were not generated for the purpose of comparing the complexity of different buildings. Currently only one method exists, Donegan’s …
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Graph Neural Networks for City Policy Recommendations as a Link Prediction Task
Graph Neural Networks (GNNs) have become a widely utilized tool in recommender systems in various contexts. While recommendation tasks can be approached using a multitude of data structures and types, graph-structured data is particularly well-suited for this domain, as graphs naturally capture a …
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A Graph Convolutional Network approach for enhancing Set Covering Problem solvers
… for large instances. This study proposes a Graph Convolutional Network (GCN) to approximate optimal solutions for SCP. A bipartite graph representation of SCP is employed to predict node priority, serving as a warm start for the Gurobi solver. The GCN is trained on solutions from a classical …
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