Add to ORKGIn this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN). This new architecture is specifically designed for regression tasks on graph-structured data that are not suitable for the well-known graph neural networks, such as the regression of functions with the domain and codomain defined on two sets of values for the vertices of a graph. In particular, we formulate a new graph-informed (GI) layer that exploits the adjacent matrix of a given graph to define the unit connections in the neural network architecture, describing a new convolution operation for inputs associated with the vertices of the graph. We study the new GINN models with ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful ...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...
Discrete Fracture Network (DFN) flow simulations are commonly used to determine the outflow in fract...
Graph-Informed Neural Networks (GINNs) present a strategy for incorporating domain knowledge into sc...
Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform infere...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-struc...
Recently, graph-based models designed for downstream tasks have significantly advanced research on g...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Convolutional neural networks (CNNs) utilize local translation invariance in the Euclidean domain an...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
This project will explore some of the most prominent Graph Neural Network variants and apply them to...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful ...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...
Discrete Fracture Network (DFN) flow simulations are commonly used to determine the outflow in fract...
Graph-Informed Neural Networks (GINNs) present a strategy for incorporating domain knowledge into sc...
Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform infere...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-struc...
Recently, graph-based models designed for downstream tasks have significantly advanced research on g...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Convolutional neural networks (CNNs) utilize local translation invariance in the Euclidean domain an...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
This project will explore some of the most prominent Graph Neural Network variants and apply them to...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful ...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...