Add to ORKGMany neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a linear graph convolution operator. Despite its simplicity, we show that our convolution operator is more theoretically grounded than many proposals in literature, and shows improved predictive performance
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural N...
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals...
We investigate whether Graph Convolutional Neural Networks (GCNNs) may benefit from incorporating in...
Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than...
Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggreg...
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data...
Many interesting problems in machine learning are being revisited with new deep learning tools. For ...
The benefit of localized features within the regular domain has given rise to the use of Convolution...
Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural n...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and field...
International audienceSince the Message Passing (Graph) Neural Networks (MPNNs) have a linear comple...
The graph neural networks have developed by leaps and bounds in recent years due to the restriction ...
Recently, many researchers have been focusing on the definition of neural networks for graphs. The b...
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural N...
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals...
We investigate whether Graph Convolutional Neural Networks (GCNNs) may benefit from incorporating in...
Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than...
Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggreg...
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data...
Many interesting problems in machine learning are being revisited with new deep learning tools. For ...
The benefit of localized features within the regular domain has given rise to the use of Convolution...
Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural n...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and field...
International audienceSince the Message Passing (Graph) Neural Networks (MPNNs) have a linear comple...
The graph neural networks have developed by leaps and bounds in recent years due to the restriction ...
Recently, many researchers have been focusing on the definition of neural networks for graphs. The b...
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural N...
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals...
We investigate whether Graph Convolutional Neural Networks (GCNNs) may benefit from incorporating in...