mlsanityData to 14 Oct 2026

MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks

arXiv 2211.12792 ↗First posted 23 Nov 2022cs.LGcited by 4

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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.

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

32 of 70 references.

References · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  7. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  8. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  9. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  10. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  11. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  12. 2019Heterogeneous Graph Attention Networkcited by 734
20 more
  1. 2020Heterogeneous Graph Transformercited by 582
  2. 2019Graph Transformer Networkscited by 523
  3. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  4. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  5. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 165
  6. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  7. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  8. 2019Unsupervised Attributed Multiplex Network Embeddingcited by 95
  9. 2019An Attention-based Graph Neural Network for Heterogeneous Structural Learningcited by 71
  10. 2021Graph Neural Networks Inspired by Classical Iterative Algorithmscited by 60
  11. 2019STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systemscited by 54
  12. 2020Understanding and Resolving Performance Degradation in Graph Convolutional Networkscited by 53
  13. 2020Interpretable and Efficient Heterogeneous Graph Convolutional Networkcited by 48
  14. 2019Heterogeneous Deep Graph Infomaxcited by 42
  15. 2020Scalable Graph Neural Networks for Heterogeneous Graphscited by 31
  16. 2021Heterogeneous Graph Representation Learning with Relation Awarenesscited by 31
  17. 2022Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networkscited by 10
  18. 2021Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-pathcited by 9
  19. 2022Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networkscited by 9
  20. 2022Graph Component Contrastive Learning for Concept Relatedness Estimationcited by 6

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learningcited by 2
  2. 2025HeTa: Relation-wise Heterogeneous Graph Foundation Attack Modelcited by 1
  3. 2025Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learningcited by 0
  4. 2025Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspectivecited by 0