# HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding

arXiv: https://arxiv.org/abs/1909.03228 · HTML: https://mlsanity.com/paper/1909.03228/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks](https://mlsanity.com/paper/2104.00853/) (2021)
- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2104.07892/) (2021)
- [Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path](https://mlsanity.com/paper/2106.09923/) (2021)
- [Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2210.00538/) (2022)
- [Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding](https://mlsanity.com/paper/2101.01425/) (2021)
- [CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling](https://mlsanity.com/paper/2110.05820/) (2021)
- [Network representation learning: A macro and micro view](https://mlsanity.com/paper/2111.10772/) (2021)
- [Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning](https://mlsanity.com/paper/2104.01711/) (2021)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration](https://mlsanity.com/paper/2504.19199/) (2025)
- [Optimizing Graph Transformer Networks with Graph-based Techniques](https://mlsanity.com/paper/2106.08500/) (2021)
- [Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs](https://mlsanity.com/paper/2210.14480/) (2022)
- [Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification](https://mlsanity.com/paper/2408.01964/) (2024)
- [Modern Structure-Aware Simplicial Spatiotemporal Neural Network](https://mlsanity.com/paper/2604.15833/) (2026)

## Abstract (arXiv)

Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attention to the higher-order Markov chain nature of meta-path guided random walks, especially to the stationarity issue. In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process, and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph, and a meta-schema respectively. We conduct extensive experiments in several heterogeneous networks and demonstrate that our methods substantially outperform the existing state-of-the-art network embedding algorithms.