Add to ORKGGraph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features,i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks,i.e., positional node classification, structural node classification, and graph classification. Extensive experimental results on 10 benchmark datasets validate our insight...
International audienceReal data collected from different applications that have additional topologic...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on...
Most Graph Neural Networks (GNNs) cannot distinguish some graphs or indeed some pairs of nodes withi...
Graph neural networks take node features and graph structure as input to build representations for n...
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks ...
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProce...
Graph convolutional network (GCN) is an effective neural network model for graph representation lear...
Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a...
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important ...
Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques ...
Graph Neural Networks (GNNs) are popular models for graph learning problems. GNNs show strong empiri...
Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs...
International audienceReal data collected from different applications that have additional topologic...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on...
Most Graph Neural Networks (GNNs) cannot distinguish some graphs or indeed some pairs of nodes withi...
Graph neural networks take node features and graph structure as input to build representations for n...
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks ...
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProce...
Graph convolutional network (GCN) is an effective neural network model for graph representation lear...
Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a...
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important ...
Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques ...
Graph Neural Networks (GNNs) are popular models for graph learning problems. GNNs show strong empiri...
Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs...
International audienceReal data collected from different applications that have additional topologic...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on...