# Heterogeneous Graph Representation Learning with Relation Awareness

arXiv: https://arxiv.org/abs/2105.11122 · HTML: https://mlsanity.com/paper/2105.11122/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [An Attention-based Graph Neural Network for Heterogeneous Structural Learning](https://mlsanity.com/paper/1912.10832/) (2019)
- [Relation Structure-Aware Heterogeneous Information Network Embedding](https://mlsanity.com/paper/1905.08027/) (2019)

## Built on by (most cited first, 30 of 31)

- [Graph Attention Multi-Layer Perceptron](https://mlsanity.com/paper/2206.04355/) (2022)
- [Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning](https://mlsanity.com/paper/2202.13538/) (2022)
- [Pretraining Language Models with Text-Attributed Heterogeneous Graphs](https://mlsanity.com/paper/2310.12580/) (2023)
- [Label-Enhanced Graph Neural Network for Semi-supervised Node Classification](https://mlsanity.com/paper/2205.15653/) (2022)
- [Meta-Path Learning for Multi-relational Graph Neural Networks](https://mlsanity.com/paper/2309.17113/) (2023)
- [Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs](https://mlsanity.com/paper/2307.03411/) (2023)
- [Collaborative Graph Neural Networks for Attributed Network Embedding](https://mlsanity.com/paper/2307.11981/) (2023)
- [Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding](https://mlsanity.com/paper/2209.11414/) (2022)
- [A Self-Explainable Heterogeneous GNN for Relational Deep Learning](https://mlsanity.com/paper/2412.00521/) (2024)
- [Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks](https://mlsanity.com/paper/2112.13078/) (2021)
- [MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2211.12792/) (2022)
- [HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.01636/) (2023)
- [A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs](https://mlsanity.com/paper/2310.04598/) (2023)
- [Graph Attention MLP with Reliable Label Utilization](https://mlsanity.com/paper/2108.10097/) (2021)
- [Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph](https://mlsanity.com/paper/2305.10771/) (2023)
- [Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining](https://mlsanity.com/paper/2412.19211/) (2024)
- [HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning](https://mlsanity.com/paper/2109.02868/) (2021)
- [Towards Efficient LLM-aware Heterogeneous Graph Learning](https://mlsanity.com/paper/2511.17923/) (2025)
- [Representation Learning on Heterostructures via Heterogeneous Anonymous Walks](https://mlsanity.com/paper/2201.06972/) (2022)
- [Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks](https://mlsanity.com/paper/2210.17142/) (2022)
- [Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph](https://mlsanity.com/paper/2312.05757/) (2023)
- [MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes](https://mlsanity.com/paper/2412.00241/) (2024)
- [MAPN: Enhancing Heterogeneous Sparse Graph Representation by Mamba-based Asynchronous Aggregation](https://mlsanity.com/paper/2502.16454/) (2025)
- [RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection](https://mlsanity.com/paper/2602.21749/) (2026)
- [RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs](https://mlsanity.com/paper/2211.11752/) (2022)
- [Generative-Contrastive Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2404.02810/) (2024)
- [SCNode: Spatial and Contextual Coordinates for Graph Representation Learning](https://mlsanity.com/paper/2410.02158/) (2024)
- [FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths](https://mlsanity.com/paper/2502.16281/) (2025)
- [A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants](https://mlsanity.com/paper/2505.23875/) (2025)
- [Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts](https://mlsanity.com/paper/2601.05537/) (2026)

## Abstract (arXiv)

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.