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.

翻译：变异图形是一种在现实生活中广泛存在的数据结构。 如今,对多元图形上的图形神经网络的研究越来越受欢迎。 现有的多元图形神经网络算法主要有两个想法, 一个是基于元病理, 另一个不是。 基于元病理的理论往往需要大量的人工预处理, 同时很难扩大到大型图。 在本文中, 我们提出了一般的混异信息传递模式, 并设计了不需要元病的 R- GSN, 与基准R- GCN相比, 已经大大改进了。 实验显示, 我们的 R- GSN 算法在 ogbbn 大型复合图谱数据集上取得了最先进的性能。