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 representation learning"”.
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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 Learning for Drug Discovery
… accelerate this process by developing machine learning (ML) algorithms for three key steps in drug discovery pipeline. First, we develop better property predictors that enable us to effectively navigate known chemical space. The main challenge is to learn a predictor based on a small, biased …
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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
This thesis focus on 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 …
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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
Machine learning methods have been widely pervasive in the domain of drug discovery, enabling more powerful and efficient models. Before deep models, modeling molecules was largely driven by expert knowledge; and to represent the complexities of the molecular landscape, these hand-engineered rules …
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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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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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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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Balancing Memory Efficiency and Accuracy in Spectral-Based Graph Transformers
… driving force behind advancements in deep learning, yet transformer-based models for graph representation learning have not caught up to mainstream Graph Neural Network (GNN) variants. A major limitation is the large O(𝑛2) memory consumption of graph transformers, where 𝑛 is the number of …
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Neural Networks on Eigenvector Data
… neural networks are provably powerful for graph representation learning, as they can approximate several classes of important functions on graphs. Our networks empirically improve machine learning models with eigenvectors, in tasks including molecular graph regression, learning expressive …
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Subgraph classification through neighborhood pooling
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Graph neural networks (GNNs) are the de facto solution for node, link, and graph-level tasks but fail to perform well on subgraph …
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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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Learning without Expert Labels for Multimodal Data
While advancements in deep learning have been largely possible due to the availability of large-scale labeled datasets, obtaining labeled datasets at the required granularity is challenging in many real-world applications, especially in scientific domains, due to the costly and labor-intensive …
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Multimodal learning and language models for enhanced knowledge representations
Machine learning with modern neural architectures, particularly large-scale language models, has enabled impressive progress across diverse problem domains such as natural language processing, graph representation learning, and tabular data analysis. However, these successes have predominantly …
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Large-scale inference and imputation for multi-tissue gene expression
… augmentation. The second study proposes two deep learning methods to study whether the complete transcriptome of a tissue can be inferred from the expression of a minimal subset of genes, with potential application in the selection of tissue-specific biomarkers and the integration of large-scale …
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SCALABLE GRAPH REPRESENTATIONAL LEARNING ALGORITHMS FOR NETWORK MEDICINE
… In this context, analyzing biomedical Knowledge Graphs that embrace bio- logical and medical concepts structured in ontologies and data generated from high- throughput bio-technologies represents a central Machine Learning and Computational Biology challenge. Indeed several compelling problems in …
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