# Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path

arXiv: https://arxiv.org/abs/2106.09923 · HTML: https://mlsanity.com/paper/2106.09923/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Hyperbolic Entailment Cones for Learning Hierarchical Embeddings](https://mlsanity.com/paper/1804.01882/) (2018)
- [End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://mlsanity.com/paper/1811.04441/) (2018)
- [PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks](https://mlsanity.com/paper/1508.00200/) (2015)
- [HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding](https://mlsanity.com/paper/1909.03228/) (2019)
- [Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks](https://mlsanity.com/paper/1807.03490/) (2018)
- [Gradient descent in hyperbolic space](https://mlsanity.com/paper/1805.08207/) (2018)
- [AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks](https://mlsanity.com/paper/1803.01848/) (2018)
- [Embedding Node Structural Role Identity into Hyperbolic Space](https://mlsanity.com/paper/2011.01512/) (2020)
- [HEAT: Hyperbolic Embedding of Attributed Networks](https://mlsanity.com/paper/1903.03036/) (2019)
- [Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network](https://mlsanity.com/paper/1808.03752/) (2018)

## Built on by (most cited first, 9 of 9)

- [Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits](https://mlsanity.com/paper/2301.00355/) (2023)
- [Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization](https://mlsanity.com/paper/2109.07016/) (2021)
- [Learning Stance Embeddings from Signed Social Graphs](https://mlsanity.com/paper/2201.11675/) (2022)
- [Hyperbolic Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2404.09456/) (2024)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2211.12792/) (2022)
- [Embedding Node Structural Role Identity Using Stress Majorization](https://mlsanity.com/paper/2109.07023/) (2021)
- [Graph-Level Embedding for Time-Evolving Graphs](https://mlsanity.com/paper/2306.01012/) (2023)
- [Why are hyperbolic neural networks effective? A study on hierarchical representation capability](https://mlsanity.com/paper/2402.02478/) (2024)

## Abstract (arXiv)

Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing heterogeneous network embedding methods suffer from the following two drawbacks: (1) The target space is usually Euclidean. Conversely, many recent works have shown that complex networks may have hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually rely on meta-paths, which require domain-specific prior knowledge for meta-path selection. Additionally, different down-streaming tasks on the same network might require different meta-paths in order to generate task-specific embeddings. In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space. We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks.