# Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

arXiv: https://arxiv.org/abs/1807.03490 · HTML: https://mlsanity.com/paper/1807.03490/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 14 of 14)

- [RelGNN: Composite Message Passing for Relational Deep Learning](https://mlsanity.com/paper/2502.06784/) (2025)
- [HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer](https://mlsanity.com/paper/2302.11329/) (2023)
- [Continual Graph Learning: A Survey](https://mlsanity.com/paper/2301.12230/) (2023)
- [Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching](https://mlsanity.com/paper/2112.08764/) (2021)
- [Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path](https://mlsanity.com/paper/2106.09923/) (2021)
- [Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://mlsanity.com/paper/2302.14061/) (2023)
- [Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)](https://mlsanity.com/paper/2202.09021/) (2022)
- [Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://mlsanity.com/paper/2210.00032/) (2022)
- [Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding](https://mlsanity.com/paper/2101.01425/) (2021)
- [Meta-Path-Free Representation Learning on Heterogeneous Networks](https://mlsanity.com/paper/2102.08120/) (2021)
- [mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding](https://mlsanity.com/paper/2104.02433/) (2021)
- [DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks](https://mlsanity.com/paper/2201.02757/) (2022)
- [HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness](https://mlsanity.com/paper/2311.03275/) (2023)
- [MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Prediction](https://mlsanity.com/paper/2501.03635/) (2025)

## Abstract (arXiv)

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.