Graph Transformer Networks: Learning meta-path graphs to improve GNNs

Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, Sean S. Yi, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs. The limitations especially become problematic when learning representations on a misspecified graph or a heterogeneous graph that consists of various types of nodes and edges. To address these limitations, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which preclude noisy connections and include useful connections (e.g., meta-paths) for tasks, while learning effective node representations on the new graphs in an end-to-end fashion. We further propose enhanced version of GTNs, Fast Graph Transformer Networks (FastGTNs), that improve scalability of graph transformations. Compared to GTNs, FastGTNs are up to 230× and 150× faster in inference and training, and use up to 100× and 148× less memory while allowing the identical graph transformations as GTNs. In addition, we extend graph transformations to the semantic proximity of nodes allowing non-local operations beyond meta-paths. Extensive experiments on both homogeneous graphs and heterogeneous graphs show that GTNs and FastGTNs with non-local operations achieve the state-of-the-art performance for node classification tasks. The code is available:

Original languageEnglish
Pages (from-to)104-119
Number of pages16
JournalNeural Networks
Publication statusPublished - 2022 Sept

Bibliographical note

Funding Information:
This research was supported by the following funding sources: National Research Foundation of Korea ( NRF-2020R1A2C3010638 ); ICT Creative Consilience program ( IITP-2022-2020-0-01819 ) and Research on CPU vulnerability detection and validation ( No.2019-0-00533 ) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation) funded by the Korea government (MSIT).

Publisher Copyright:
© 2022 The Authors


  • Graph Neural Networks
  • Heterogeneous graphs
  • Machine learning on graphs
  • Network analysis

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Graph Transformer Networks: Learning meta-path graphs to improve GNNs'. Together they form a unique fingerprint.

Cite this