The recent past has seen an increasing interest in Heterogeneous Graph Neural
Networks (HGNNs) since many real-world graphs are heterogeneous in nature, from
citation graphs to email graphs. However, existing methods ignore a tree
hierarchy among metapaths, which is naturally constituted by different node
types and relation types. In this paper, we present HetTree, a novel
heterogeneous tree graph neural network that models both the graph structure
and heterogeneous aspects in a scalable and effective manner. Specifically,
HetTree builds a semantic tree data structure to capture the hierarchy among
metapaths. Existing tree encoding techniques aggregate children nodes by
weighting the contribution of children nodes based on similarity to the parent
node. However, we find that this tree encoding fails to capture the entire
parent-children hierarchy by only considering the parent node. Hence, HetTree
uses a novel subtree attention mechanism to emphasize metapaths that are more
helpful in encoding parent-children relationships. Moreover, instead of
separating feature learning from label learning or treating features and labels
equally by projecting them to the same latent space, HetTree proposes to match
them carefully based on corresponding metapaths, which provides more accurate
and richer information between node features and labels. Our evaluation of
HetTree on a variety of real-world datasets demonstrates that it outperforms
all existing baselines on open benchmarks and efficiently scales to large
real-world graphs with millions of nodes and edges.

本文提出了 HetTree，一种新颖的异构树图神经网络，以一种可扩展且有效的方式对图结构和异构性进行建模，通过构建一个语义树数据结构来捕捉元路径之间的层次关系，使用子树注意机制强调更有助于编码父子关系的元路径，并通过匹配相应的元路径仔细处理节点特征和标签，从而提供了更准确和丰富的信息

HetTree：异构树图神经网络

HetTree: Heterogeneous Tree Graph Neural Network

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。