02.09.2021 · A set of objects, and the connections between them, are naturally expressed as a graph. Researchers have developed neural networks that operate on graph data (called graph neural networks, or GNNs) for over a decade. Recent developments have increased their capabilities and expressive power.
Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed ...
Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants ...
03.01.2022 · Graph Neural Network (GNN) is a relatively modern deep learning approach that falls under the domain of neural networks that focuses on processing data on graphs to make complicated graph data...
A graph neural network (GNN) is a class of neural networks for processing data represented by graph data structures. ... They were popularized by their use in ...
08.02.2021 · Graph neural networks (GNNs) is a subtype of neural networks that operate on data structured as graphs. By enabling the application of deep learning to graph-structured data, GNNs are set to become an important artificial intelligence (AI) concept in future.