# A Tutorial on Network Embeddings

arXiv: https://arxiv.org/abs/1808.02590 · HTML: https://mlsanity.com/paper/1808.02590/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 12 of 12)

- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023)
- [Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts](https://mlsanity.com/paper/2210.16062/) (2022)
- [Adversarial Attack on Network Embeddings via Supervised Network Poisoning](https://mlsanity.com/paper/2102.07164/) (2021)
- [A Survey on Role-Oriented Network Embedding](https://mlsanity.com/paper/2107.08379/) (2021)
- [Independent Distribution Regularization for Private Graph Embedding](https://mlsanity.com/paper/2308.08360/) (2023)
- [H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces](https://mlsanity.com/paper/2304.06970/) (2023)
- [Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft](https://mlsanity.com/paper/2202.02385/) (2022)
- [Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings](https://mlsanity.com/paper/2212.08046/) (2022)
- [HUGE: Huge Unsupervised Graph Embeddings with TPUs](https://mlsanity.com/paper/2307.14490/) (2023)
- [WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows](https://mlsanity.com/paper/2111.10928/) (2021)
- [DynaSTy: A Framework for SpatioTemporal Node Attribute Prediction in Dynamic Graphs](https://mlsanity.com/paper/2601.05391/) (2026)

## Abstract (arXiv)

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.