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Graph Transformer Networks - NeurIPS

Graph Transformer NetworksSeongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang , Hyunwoo J. Kim Department of Computer Science and EngineeringKorea University{ysj5419, minbyuljeong, raehyun, kangj, neural Networks (GNNs) have been widely used in representation learning ongraphs and achieved state-of-the-art performance in tasks such as node classificationand link prediction. However, most existing GNNs are designed to learn noderepresentations on thefixedandhomogeneousgraphs. The limitations especiallybecome problematic when learning representations on a misspecified Graph oraheterogeneousgraph that consists of various types of nodes and edges.}

heterogeneous graph and learns node representations via convolution on the learnt graph structures for a given problem. Our contributions are as follows:(i)We propose a novel framework Graph Transformer Networks, to learn a new graph structure which involves identifying useful meta-paths and multi-hop connections

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