Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
Recently, bearing the message passing paradigm, graph neural networks(GNNs) have greatly advanced the performance of node representation learning on graphs. However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. Also, …