Heterogeneous graph neural network has unleashed great potential on graph
representation learning and shown superior performance on downstream tasks such
as node classification and clustering. Existing heterogeneous graph learning
networks are primarily designed to either rely on pre-defined meta-paths or use
attention mechanisms for type-specific attentive message propagation on
different nodes/edges, incurring many customization efforts and computational
costs. To this end, we design a relation-centered Pooling and Convolution for
Heterogeneous Graph learning Network, namely PC-HGN, to enable
relation-specific sampling and cross-relation convolutions, from which the
structural heterogeneity of the graph can be better encoded into the embedding
space through the adaptive training process. We evaluate the performance of the
proposed model by comparing with state-of-the-art graph learning models on
three different real-world datasets, and the results show that PC-HGN
consistently outperforms all the baseline and improves the performance
maximumly up by 17.8%.

提出了一种基于关系聚合和卷积 (PC-HGN) 的异构图神经网络模型，它可以进行关系特定的采样和跨关系卷积，从而更好地将图的结构异质性编码到嵌入空间中，与其他现有的图学习模型相比，其性能得到了最大 17.8％的提升。