Accordingly, one very popular GNN architecture is the graph convolutional neural network (GCN), which uses convolution layers to create graph embeddings. Applications of graph neural networks ...

Once trained, the GNN can be used for inference, making predictions for tasks, such as node classification, link prediction or graph classification. Graph neural networks vs. graph convolutional ...

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is ...

Pinterest today shared details about how it created PinSage, a graph convolutional network that can learn about things like nearby Pins, or nodes, in massive web-scale graphs.