A large number of real-world graphs or networks are inherently heterogeneous,
involving a diversity of node types and relation types. Heterogeneous graph
embedding is to embed rich structural and semantic information of a
heterogeneous graph into low-dimensional node representations. Existing models
usually define multiple metapaths in a heterogeneous graph to capture the
composite relations and guide neighbor selection. However, these models either
omit node content features, discard intermediate nodes along the metapath, or
only consider one metapath. To address these three limitations, we propose a
new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the
final performance. Specifically, MAGNN employs three major components, i.e.,
the node content transformation to encapsulate input node attributes, the
intra-metapath aggregation to incorporate intermediate semantic nodes, and the
inter-metapath aggregation to combine messages from multiple metapaths.
Extensive experiments on three real-world heterogeneous graph datasets for node
classification, node clustering, and link prediction show that MAGNN achieves
more accurate prediction results than state-of-the-art baselines.

提出了一种新的叫做元路径聚合图神经网络（MAGNN）的模型来解决异构图嵌入中节点内容特征、中间节点和元路径的问题，并在三个真实异构图数据集上进行了实验，证明了 MAGNN 比现有基线模型具有更准确的预测结果。