# MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks

arXiv: https://arxiv.org/abs/2211.12792 · HTML: https://mlsanity.com/paper/2211.12792/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 70 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [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)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs](https://mlsanity.com/paper/1803.07294/) (2018)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Unsupervised Attributed Multiplex Network Embedding](https://mlsanity.com/paper/1911.06750/) (2019)
- [An Attention-based Graph Neural Network for Heterogeneous Structural Learning](https://mlsanity.com/paper/1912.10832/) (2019)
- [Graph Neural Networks Inspired by Classical Iterative Algorithms](https://mlsanity.com/paper/2103.06064/) (2021)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [Understanding and Resolving Performance Degradation in Graph Convolutional Networks](https://mlsanity.com/paper/2006.07107/) (2020)
- [Interpretable and Efficient Heterogeneous Graph Convolutional Network](https://mlsanity.com/paper/2005.13183/) (2020)
- [Heterogeneous Deep Graph Infomax](https://mlsanity.com/paper/1911.08538/) (2019)
- [Scalable Graph Neural Networks for Heterogeneous Graphs](https://mlsanity.com/paper/2011.09679/) (2020)
- [Heterogeneous Graph Representation Learning with Relation Awareness](https://mlsanity.com/paper/2105.11122/) (2021)
- [Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks](https://mlsanity.com/paper/2203.02018/) (2022)
- [Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path](https://mlsanity.com/paper/2106.09923/) (2021)

## Built on by (most cited first, 4 of 4)

- [Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2407.20648/) (2024)
- [HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model](https://mlsanity.com/paper/2506.07428/) (2025)
- [Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2503.13911/) (2025)
- [Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective](https://mlsanity.com/paper/2510.05750/) (2025)

## Abstract (arXiv)

Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts, a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency.