mlsanityData to 7 Oct 2026

R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph

arXiv 2103.07877 ↗First posted 14 Mar 2021cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular. The existing heterogeneous graph neural network algorithms mainly have two ideas, one is based on meta-path and the other is not. The idea based on meta-path often requires a lot of manual preprocessing, at the same time it is difficult to extend to large scale graphs. In this paper, we proposed the general heterogeneous message passing paradigm and designed R-GSN that does not need meta-path, which is much improved compared to the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves the state-of-the-art performance on the ogbn-mag large scale heterogeneous graph dataset.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 14 Mar 2021.

Builds on

16 of 20 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
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4 more
  1. 2020Heterogeneous Graph Transformercited by 581
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2022Graph Attention Multi-Layer Perceptroncited by 82
  2. 2022Label-Enhanced Graph Neural Network for Semi-supervised Node Classificationcited by 11
  3. 2022Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embeddingcited by 7
  4. 2021Graph Attention MLP with Reliable Label Utilizationcited by 3
  5. 2022Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Userscited by 2
  6. 2026Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Expertscited by 0