Add to ORKGGraph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performance have not been well understood. In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. While they improve the scalability, their training times still depend on the implementations in the frameworks as sampling and its associated operations can introduc...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in ...
The demonstrated success of transfer learning has popularized approaches that involve pretraining mo...
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing a...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable represen...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and perfo...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Graph neural networks (GNNs) can extract features by learning both the representation of each object...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Graphs can be used to represent many important classes of structured real-world data. For this reaso...
Graph neural networks (GNNs) have been demonstrated as a powerful tool for analysing non-Euclidean g...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in ...
The demonstrated success of transfer learning has popularized approaches that involve pretraining mo...
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing a...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable represen...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and perfo...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Graph neural networks (GNNs) can extract features by learning both the representation of each object...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Graphs can be used to represent many important classes of structured real-world data. For this reaso...
Graph neural networks (GNNs) have been demonstrated as a powerful tool for analysing non-Euclidean g...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in ...
The demonstrated success of transfer learning has popularized approaches that involve pretraining mo...