🕸What is Graph Transformer and how does this NN-model works
Transformers based neural networks in NLP-tasks overcome the bottlenecks of Recurrent Neural Networks (RNNs) caused by the sequential processing. Mapping the words in a sentence and combines the received information they can generate its abstract feature representations.
For learning on graphs, graph neural networks (GNNs) with several parameterized layers have emerged as the most powerful tool in deep learning. Each GNN-layer takes a graph with node (and edge) features and builds abstract feature representations of nodes (and edges) based the available explicit connectivity structure (graph structure). The so-generated features are then passed to downstream classification layers and the target property is predicted. Generalization of transformer neural networks to graphs can learn on graphs and datasets with arbitrary structure rather than just the sequential as NLP-transformers.
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