Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
GNNs have been widely used in deep learning on graphs. They learn effective node representations. However, most methods ignore the heterogeneity. Methods designed for heterogeneous graphs, on the other hand, fail to learn complex semantic representations because they only use meta-paths instead of meta-graphs. Furthermore, they cannot fully capture the …