Graph Transformer Networks - NeurIPS

remarkable success in representation learning, GNNs learn a powerful representation for given tasks and data. To improve performance or scalability, generalized convolution based on spectral convolution [4, 26], attention mechanism on neighbors [25, 33], subsampling [6, 7] and inductive representation for a large graph [14] have been studied.

  Large, Learning, Representation, Transformers, Inductive, Representation learning, Inductive representation