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Heterogeneous graph neural networks (HGNNs) have been widely applied inheterogeneous information network tasks, while most HGNNs suffer from poorscalability or weak representation when they are applied to large-scaleheterogeneous graphs. To address these problems, we propose a novelRelation-aware Heterogeneous Graph Neural Network with Contrastive Learning(RHCO) for large-scale heterogeneous graph representation learning. Unliketraditional heterogeneous graph neural networks, we adopt the contrastivelearning mechanism to deal with the complex heterogeneity of large-scaleheterogeneous graphs. We first learn relation-aware node embeddings under thenetwork schema view. Then we propose a novel positive sample selection strategyto choose meaningful positive samples. After learning node embeddings under thepositive sample graph view, we perform a cross-view contrastive learning toobtain the final node representations. Moreover, we adopt the label smoothingtechnique to boost the performance of RHCO. Extensive experiments on threelarge-scale academic heterogeneous graph datasets show that RHCO achieves bestperformance over the state-of-the-art models.

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs