Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.

利用元路径识别异质图中的异质性并提出了两个度量方法来定量描述异质性水平，在多个具有不同异质性水平的真实异质图上进行了深入研究，观察到继承自处理同质图的图神经网络对异质图的异质性或同质性较低的泛化能力有限。为解决这一挑战，提出了一种新的异质图神经网络 Hetero$^2$Net，通过蒙版元路径预测和蒙版标签预测任务，有效且灵活地处理同质性和异质性异质图，在五个具有不同异质性水平的真实异质图基准上评估了 Hetero$^2$Net 的性能，结果表明 Hetero$^2$Net 在半监督节点分类任务中优于强基线模型, 为有效处理更复杂的异质图提供了有价值的见解。

Hetero$^2$Net: 非同质图上的异质感知表示学习