Collaborative representation learning for nodes and relations via heterogeneous graph neural network

Weimin Li*, Lin Ni, Jianjia Wang, Can Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly suitable for characterizing real-world complex systems. In recent years, due to their strong capability of capturing rich semantics, heterogeneous graph neural networks (HGNNs) have proven to be a powerful technique for representation learning on heterogeneous graphs. However, most of the existing HGNNs only focus on learning node representations and ignore the learning of relation representations, which are complementary to node representations. To address this limitation, we propose a new HGNN model with Collaborative Representation Learning for Nodes and Relations (named CoNR) for link prediction task in this paper. Collaborative learning means that node representations and relation representations participate in and affect each other's learning process. Specifically, node representations are obtained through a delicate two-step attention mechanism incorporating relation representations that can hierarchically aggregate information within one relation and across different relations. For relation representations, a relation encoder based on node information is designed to encode node representations into relation representations. Therefore, in this framework, node representations and relation representations are mutually updated in a layer-wise manner and work together to facilitate the downstream tasks better. Extensive experimental results on different datasets show the excellent performance of the proposed CoNR.

Original languageEnglish
Article number109673
JournalKnowledge-Based Systems
Publication statusPublished - 14 Nov 2022
Externally publishedYes


  • Collaborative
  • Graph neural networks
  • Heterogeneous graph
  • Representation learning


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