# Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks

arXiv: https://arxiv.org/abs/2012.10024 · HTML: https://mlsanity.com/paper/2012.10024/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Heterogeneous Graph Masked Autoencoders](https://mlsanity.com/paper/2208.09957/) (2022)
- [Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations](https://mlsanity.com/paper/2210.08345/) (2022)
- [Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network](https://mlsanity.com/paper/2402.11518/) (2024)
- [Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2206.11081/) (2022)
- [GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation](https://mlsanity.com/paper/2403.15520/) (2024)
- [Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples](https://mlsanity.com/paper/2212.13847/) (2022)
- [Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2304.12228/) (2023)
- [Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs](https://mlsanity.com/paper/2401.03597/) (2024)
- [HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning](https://mlsanity.com/paper/2109.02868/) (2021)
- [Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping](https://mlsanity.com/paper/2201.03340/) (2022)
- [Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning](https://mlsanity.com/paper/2310.11102/) (2023)
- [Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes](https://mlsanity.com/paper/2311.07929/) (2023)
- [M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2309.01101/) (2023)
- [Heterogeneous Graph Contrastive Learning with Spectral Augmentation](https://mlsanity.com/paper/2407.00708/) (2024)
- [LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs](https://mlsanity.com/paper/2409.06323/) (2024)
- [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)

A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types. We study the problem of classifying objects in HINs. Most existing methods perform poorly when given scarce labeled objects as training sets, and methods that improve classification accuracy under such scenarios are often computationally expensive. To address these problems, we propose ConCH, a graph neural network model. ConCH formulates the classification problem as a multi-task learning problem that combines semi-supervised learning with self-supervised learning to learn from both labeled and unlabeled data. ConCH employs meta-paths, which are sequences of object types that capture semantic relationships between objects. ConCH co-derives object embeddings and context embeddings via graph convolution. It also uses the attention mechanism to fuse such embeddings. We conduct extensive experiments to evaluate the performance of ConCH against other 15 classification methods. Our results show that ConCH is an effective and efficient method for HIN classification.