Heterogeneous graph neural networks (HGNNs) have been widely applied in
heterogeneous information network tasks, while most HGNNs suffer from poor
scalability or weak representation when they are applied to large-scale
heterogeneous graphs. To address these problems, we propose a novel
Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning
(RHCO) for large-scale heterogeneous graph representation learning. Unlike
traditional heterogeneous graph neural networks, we adopt the contrastive
learning mechanism to deal with the complex heterogeneity of large-scale
heterogeneous graphs. We first learn relation-aware node embeddings under the
network schema view. Then we propose a novel positive sample selection strategy
to choose meaningful positive samples. After learning node embeddings under the
positive sample graph view, we perform a cross-view contrastive learning to
obtain the final node representations. Moreover, we adopt the label smoothing
technique to boost the performance of RHCO. Extensive experiments on three
large-scale academic heterogeneous graph datasets show that RHCO achieves best
performance over the state-of-the-art models.

本文提出了一种新的基于对比学习的关系感知异构图神经网络 RHCO，用于大规模异构图表示学习，相对于传统的异构图神经网络，可以更好地处理复杂异构网络结构，并采用标签平滑技术提高了性能，实验证明在三个大规模学术异构图数据集上 RHCO 取得了最好的性能表现。