Hyperbolic Graph Transformer Networks for non-Euclidean Data Analysis on Heterogeneous Graphs

Seunghun Lee, Hyeonjin Park, Hyunwoo J Kim

http://doi.org/10.5626/JOK.2021.48.2.217

Convolution Neural Networks (CNNs), which are based on convolution operations, are used for various tasks in image classification, image generation, time series analysis, etc. Since the convolution operations are not directly applicable to non-Euclidean spaces such as graphs and manifolds, a variety of Graph Neural Networks (GNNs) have extended convolutional neural networks to homogeneous graphs, which has a single type of edges and nodes. However, in real-world applications, heterogeneous and hierarchical graph data often occur. To expand the operating range of GNNs to the graphs that have multiple types of nodes and edges with the hierarchy, herein, we propose a new model that integrates Hyperbolic Graph Convolution Networks (HGCNs) and Graph Transformer Networks (GTNs).