Add to ORKGGraph neural networks (GNNs) have evolved into one of the most popular deep learning architectures. However, GNNs suffer from over-smoothing node information and, therefore, struggle to solve tasks where global graph properties are relevant. We introduce G-Signatures, a novel graph learning method that enables global graph propagation via randomized signatures. G-Signatures use a new graph conversion concept to embed graph structured information which can be interpreted as paths in latent space. We further introduce the idea of latent space path mapping. This allows us to iteratively traverse latent space paths, and, thus globally process information. G-Signatures excel at extracting and processing global graph properties, and effectively s...
Given a large graph with few node labels, how can we (a) identify whether there is generalized netwo...
End-to-end training of graph neural networks (GNN) on large graphs presents several memory and compu...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. H...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur perfor...
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural Networks (CNNs), are typicall...
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performanc...
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classif...
Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on ...
Given a large graph with few node labels, how can we (a) identify whether there is generalized netwo...
End-to-end training of graph neural networks (GNN) on large graphs presents several memory and compu...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. H...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur perfor...
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural Networks (CNNs), are typicall...
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performanc...
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classif...
Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on ...
Given a large graph with few node labels, how can we (a) identify whether there is generalized netwo...
End-to-end training of graph neural networks (GNN) on large graphs presents several memory and compu...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...