# Simple and Efficient Heterogeneous Graph Neural Network

arXiv: https://arxiv.org/abs/2207.02547 · HTML: https://mlsanity.com/paper/2207.02547/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 56 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [SIGN: Scalable Inception Graph Neural Networks](https://mlsanity.com/paper/2004.11198/) (2020)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources](https://mlsanity.com/paper/2011.14867/) (2020)
- [Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels](https://mlsanity.com/paper/1902.11038/) (2019)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Graph Attention Multi-Layer Perceptron](https://mlsanity.com/paper/2206.04355/) (2022)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [An Attention-based Graph Neural Network for Heterogeneous Structural Learning](https://mlsanity.com/paper/1912.10832/) (2019)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training](https://mlsanity.com/paper/2104.09376/) (2021)
- [Sampling methods for efficient training of graph convolutional networks: A survey](https://mlsanity.com/paper/2103.05872/) (2021)

## Built on by (most cited first, 30 of 71)

- [Learning on Multimodal Graphs: A Survey](https://mlsanity.com/paper/2402.05322/) (2024)
- [Homophily-oriented Heterogeneous Graph Rewiring](https://mlsanity.com/paper/2302.06299/) (2023)
- [Efficient Heterogeneous Graph Learning via Random Projection](https://mlsanity.com/paper/2310.14481/) (2023)
- [Topology-aware Embedding Memory for Continual Learning on Expanding Networks](https://mlsanity.com/paper/2401.13200/) (2024)
- [SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning](https://mlsanity.com/paper/2406.11389/) (2024)
- [Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://mlsanity.com/paper/2302.14061/) (2023)
- [Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization](https://mlsanity.com/paper/2305.09938/) (2023)
- [Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials](https://mlsanity.com/paper/2305.19872/) (2023)
- [HGAttack: Transferable Heterogeneous Graph Adversarial Attack](https://mlsanity.com/paper/2401.09945/) (2024)
- [LinkSAGE: Optimizing Job Matching Using Graph Neural Networks](https://mlsanity.com/paper/2402.13430/) (2024)
- [Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling](https://mlsanity.com/paper/2403.05752/) (2024)
- [A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks](https://mlsanity.com/paper/2406.13125/) (2024)
- [HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.01636/) (2023)
- [Long-range Meta-path Search on Large-scale Heterogeneous Graphs](https://mlsanity.com/paper/2307.08430/) (2023)
- [RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation](https://mlsanity.com/paper/2408.07331/) (2024)
- [OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2504.17811/) (2025)
- [Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph](https://mlsanity.com/paper/2305.10771/) (2023)
- [Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models](https://mlsanity.com/paper/2405.18581/) (2024)
- [HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter](https://mlsanity.com/paper/2410.07854/) (2024)
- [Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity](https://mlsanity.com/paper/2508.06034/) (2025)
- [Knowledge Enhanced Graph Neural Networks for Graph Completion](https://mlsanity.com/paper/2303.15487/) (2023)
- [Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack](https://mlsanity.com/paper/2403.07943/) (2024)
- [HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2407.13158/) (2024)
- [ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion](https://mlsanity.com/paper/2504.15920/) (2025)
- [Heterogeneous Temporal Hypergraph Neural Network](https://mlsanity.com/paper/2506.17312/) (2025)
- [Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs](https://mlsanity.com/paper/2506.20980/) (2025)
- [Simple and Efficient Heterogeneous Temporal Graph Neural Network](https://mlsanity.com/paper/2510.18467/) (2025)
- [An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs](https://mlsanity.com/paper/2603.04545/) (2026)
- [Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning](https://mlsanity.com/paper/2310.11102/) (2023)
- [Graph Neural Networks with a Distribution of Parametrized Graphs](https://mlsanity.com/paper/2310.16401/) (2023)

## Abstract (arXiv)

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.