# Network Representation Learning: A Survey

arXiv: https://arxiv.org/abs/1801.05852 · HTML: https://mlsanity.com/paper/1801.05852/

## Key facts

- Cited by 101 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 30 of 101)

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- [Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs](https://mlsanity.com/paper/2305.12334/) (2023)
- [Collaborative Graph Neural Networks for Attributed Network Embedding](https://mlsanity.com/paper/2307.11981/) (2023)
- [GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding](https://mlsanity.com/paper/2110.06196/) (2021)
- [Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters](https://mlsanity.com/paper/2402.00608/) (2024)
- [Deep Cut-informed Graph Embedding and Clustering](https://mlsanity.com/paper/2503.06635/) (2025)
- [Persistent Homology and Graphs Representation Learning](https://mlsanity.com/paper/2102.12926/) (2021)
- [Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning](https://mlsanity.com/paper/2109.11053/) (2021)
- [Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey](https://mlsanity.com/paper/2204.08226/) (2022)
- [GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection](https://mlsanity.com/paper/2303.17334/) (2023)

## Abstract (arXiv)

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.