  Heterogeneous graph is a kind of data structure widely existing in real life.
Nowadays, the research of graph neural network on heterogeneous graph has
become more and more popular. The existing heterogeneous graph neural network
algorithms mainly have two ideas, one is based on meta-path and the other is
not. The idea based on meta-path often requires a lot of manual preprocessing,
at the same time it is difficult to extend to large scale graphs. In this
paper, we proposed the general heterogeneous message passing paradigm and
designed R-GSN that does not need meta-path, which is much improved compared to
the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves
the state-of-the-art performance on the ogbn-mag large scale heterogeneous
graph dataset.
