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 7 of 7 for “"Heterogeneous graph."”.
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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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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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Graph Neural Networks for Health-Aware Food and Multi-Criteria Recommendation Systems
… Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA) and the Multiview Graph Dual Attention and Contrastive Learning for Multi-Criteria Recommender Systems (D-MGAC). The first framework, HFRS-DA, addresses the challenge of effectively integrating heterogeneous …
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Efficient Robotic Manipulation with Scene Knowledge
… we improved the robot's efficiency by employing graph neural networks (GNN) to exploit the underlying relationships in the scene. To accomplish complex manipulation tasks in constrained environments, such as rearranging adversarial objects, we hierarchically integrated a heterogeneous graph …
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Structure-aware Deep Learning
Graph structures permeate the digital landscape in explicit and implicit forms. They connect or construct artifacts by combining semantic and structural information. We also observe them in the systems designed to process this data, in their learning algorithms and the very nature of the tasks they …
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Accelerating graph computation with system optimizations and algorithmic design
… data in today's world can be represented in a graph form, and these graphs can then be used as input to graph applications to derive useful information, such as shortest paths in a road network, similarity between drugs in a drug-protein network, persons of interest in a social network, or …
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An integrative and systems biology approach to interpreting, prioritizing, and analyzing the genetics of complex disorders.
… sparse association data. Hence, highly scalable graph algorithms, which combine features among all aspects of omics data, are leveraged to investigate the genomic underpinnings of this complex and multifactorial disorder. Described in these studies is an investigation of classes of SUDs, namely …