# MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

arXiv: https://arxiv.org/abs/1803.02533 · HTML: https://mlsanity.com/paper/1803.02533/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 13 of 13)

- [Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2104.07892/) (2021)
- [Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model](https://mlsanity.com/paper/2109.02058/) (2021)
- [Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification](https://mlsanity.com/paper/2202.05932/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Source-Aware Embedding Training on Heterogeneous Information Networks](https://mlsanity.com/paper/2307.04336/) (2023)
- [HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs](https://mlsanity.com/paper/2404.00816/) (2024)
- [HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2407.13158/) (2024)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine Translation](https://mlsanity.com/paper/2403.03521/) (2024)
- [A Heterogeneous Graph Learning Model for Cyber-Attack Detection](https://mlsanity.com/paper/2112.08986/) (2021)
- [Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information](https://mlsanity.com/paper/2309.11294/) (2023)
- [Heterogeneous Graph Contrastive Learning with Spectral Augmentation](https://mlsanity.com/paper/2407.00708/) (2024)
- [Multi-Granular Attention based Heterogeneous Hypergraph Neural Network](https://mlsanity.com/paper/2505.04340/) (2025)

## Abstract (arXiv)

Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result, conventional network embedding techniques cannot work on such HINs. Recently, metapath-based approaches have been proposed to characterize relationships in HINs, but they are ineffective in capturing rich contexts and semantics between nodes for embedding learning, mainly because (1) metapath is a rather strict single path node-node relationship descriptor, which is unable to accommodate variance in relationships, and (2) only a small portion of paths can match the metapath, resulting in sparse context information for embedding learning. In this paper, we advocate a new metagraph concept to capture richer structural contexts and semantics between distant nodes. A metagraph contains multiple paths between nodes, each describing one type of relationships, so the augmentation of multiple metapaths provides an effective way to capture rich contexts and semantic relations between nodes. This greatly boosts the ability of metapath-based embedding techniques in handling very sparse HINs. We propose a new embedding learning algorithm, namely MetaGraph2Vec, which uses metagraph to guide the generation of random walks and to learn latent embeddings of multi-typed HIN nodes. Experimental results show that MetaGraph2Vec is able to outperform the state-of-the-art baselines in various heterogeneous network mining tasks such as node classification, node clustering, and similarity search.