Heterogeneous Graph Representation Learning with Relation Awareness

TL;DR

This paper introduces R-HGNN, a relation-aware graph neural network that learns fine-grained node representations on heterogeneous graphs by modeling relation semantics and interactions, improving performance on various tasks.

Contribution

The paper proposes a novel relation-aware GNN that explicitly models relation semantics and cross-relation interactions for better node representations in heterogeneous graphs.

Findings

R-HGNN outperforms existing methods on multiple graph learning tasks.

The relation-aware modules improve the quality of node embeddings.

Layer-wise relation learning captures semantic nuances effectively.

Abstract

Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link prediction. Existing heterogeneous graph learning methods are primarily developed by following the propagation mechanism of node representations. There are few efforts on studying the role of relations for improving the learning of more fine-grained node representations. Indeed, it is important to collaboratively learn the semantic representations of relations and discern node representations with respect to different relation types. To this end, in this paper, we propose a novel Relation-aware Heterogeneous Graph Neural Network, namely R-HGNN, to learn node representations on heterogeneous graphs at a fine-grained level by considering relation-aware characteristics. Specifically, a dedicated graph convolution component is first designed to learn unique node representations from each relation-specific graph separately. Then, a cross-relation message passing module is developed to improve the interactions of node representations across different relations. Also, the relation representations are learned in a layer-wise manner to capture relation semantics, which are used to guide the node representation learning process. Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations. Finally, we conduct extensive experiments on a variety of graph learning tasks, and experimental results demonstrate that our approach consistently outperforms existing methods among all the tasks.