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 15 of 15 for “"Graph Convolution"”.
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Molecular graph Self attention and graph convolution for drug discovery
… development. We model molecules as undirected graphs and use graph convolutions and self-attention to predict molecular properties. With a series of ablation studies, we demonstrate the added value of several key components in our network. We analyze two standard datasets: BBBP, which includes …
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Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution
… and a couple different approaches that use Graph Neural Networks to identify transit disruption related information in Tweets from a live Twitter stream related to the Washington Metropolitan Area Transit Authority (WMATA) metro system. After developing three different models, a Dynamic …
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Leveraging Basis Alignment to create a Generalized Multi-Relational Graph Convolution Network in the Federated Setting
Knowledge graphs have seen a significant rise in popularity and usage in recent years with many real-world applications taking advantage of their ability to model interlinked data easily. In general, many institutions maintain their own knowledge graphs, however these graphs tend to suffer from …
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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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Deep Learning Models for Traffic Prediction in Urban Transport Networks.
… a deep learning model (named SAGCN-SST) based on Graph Convolution Network (GCN) and Attention mechanism for multiple traffic speed prediction on large-scale road networks. This SAGCN- SST is able to capture dynamic-spatial and temporal features for the final prediction. The predicted traffic …
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Dynamic Spatio-Temporal Graph Convolutional Networks
… have seen impressive gains in the performance of graph learning as a paradigm for spatial learning problems. Some recent work has explored the intersection of these two fields but often assumes that the underlying graph structure is static. We introduce Dynamic Spatio-Temporal Graph Convolution …
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VRAG: Region Attention Graph for Content-Based Video Retrieval
… this paper, we introduce Video Region Attention Graph Networks (VRAG) that improves the state-of-the-art of video-level methods. We represent videos at a finer granularity viaregion-level features and encode video spatio-temporal dy-namics through region-level relations. Our VRAG capturesthe …
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep neural network model which captures the …
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Building Footprint Reconstruction from Satellite Imagery Using a Deep Learning Framework with Geometric Regularization
… advanced deep learning model, that leverages Graph Convolution Network (GCN) to enhance building footprint reconstruction. By incorporating geometric regularity, multi-scale feature fusion, and Attraction Field Maps (AFM), the model generates more structured and precise building outlines from …
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Supervised Inference of Gene Regulatory Networks
… 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 low-dimensional embedding of the nodes …
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Modelling Group Recommender Systems
… at this order. Here we offer a novel Graph Convolution Network - MBGCN applying influence bias to distinguish neighbors' impacts on users and items. 3) Not limited to social networks, group relationships need to be expanded to a larger scope. It is unclear how we may obtain the …
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Data-Driven Voltage Control of DERs Integrated Distribution Grids Using Deep Reinforcement Learning
… data. Therefore, incorporating graph convolution layers and generative adversarial network with DRL algorithm, this research address these problems. The implementation of the existing DRL based approaches in real power grids has been impeded by the absence of clear assurances …
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Visual question answering using external knowledge
… to misconceptions due to synonyms and homographs. To overcome these shortcomings, we introduce two new approaches in this work. We develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging …
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User behavior modeling: Towards solving the duality of interpretability and precision
… the quality of the answer. We also develop convolution operators to encode these semantically different graphs and later merge them using boosting. We also proposed an alternative approach to incorporate user behavioral information by jointly estimating the latent behavioral representations …
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Identifying and Minimizing Underspecification in Breast Cancer Subtyping
<p>In the realm of biomedical technology, both accuracy and consistency are crucial to the development and deployment of these tools. While accuracy is easy to measure, consistency metrics are not so simple to measure, especially in the scope of biomedicine where prediction consistency can be …