# A Survey on Dynamic Network Embedding

arXiv: https://arxiv.org/abs/2006.08093 · HTML: https://mlsanity.com/paper/2006.08093/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities](https://mlsanity.com/paper/2302.01018/) (2023)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [Dynamic Network Embedding Survey](https://mlsanity.com/paper/2103.15447/) (2021)
- [Spectral embedding for dynamic networks with stability guarantees](https://mlsanity.com/paper/2106.01282/) (2021)
- [A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks](https://mlsanity.com/paper/2205.09263/) (2022)
- [TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers](https://mlsanity.com/paper/2307.02588/) (2023)
- [DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs](https://mlsanity.com/paper/2109.13441/) (2021)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Network Representation Learning: From Traditional Feature Learning to Deep Learning](https://mlsanity.com/paper/2103.04339/) (2021)
- [Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)](https://mlsanity.com/paper/2108.03400/) (2021)

## Abstract (arXiv)

Real-world networks are composed of diverse interacting and evolving entities, while most of existing researches simply characterize them as particular static networks, without consideration of the evolution trend in dynamic networks. Recently, significant progresses in tracking the properties of dynamic networks have been made, which exploit changes of entities and links in the network to devise network embedding techniques. Compared to widely proposed static network embedding methods, dynamic network embedding endeavors to encode nodes as low-dimensional dense representations that effectively preserve the network structures and the temporal dynamics, which is beneficial to multifarious downstream machine learning tasks. In this paper, we conduct a systematical survey on dynamic network embedding. In specific, basic concepts of dynamic network embedding are described, notably, we propose a novel taxonomy of existing dynamic network embedding techniques for the first time, including matrix factorization based, Skip-Gram based, autoencoder based, neural networks based and other embedding methods. Additionally, we carefully summarize the commonly used datasets and a wide variety of subsequent tasks that dynamic network embedding can benefit. Afterwards and primarily, we suggest several challenges that the existing algorithms faced and outline possible directions to facilitate the future research, such as dynamic embedding models, large-scale dynamic networks, heterogeneous dynamic networks, dynamic attributed networks, task-oriented dynamic network embedding and more embedding spaces.