# Graph Representation Learning: A Survey

arXiv: https://arxiv.org/abs/1909.00958 · HTML: https://mlsanity.com/paper/1909.00958/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 30 of 75)

- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Graph Transformers: A Survey](https://mlsanity.com/paper/2407.09777/) (2024)
- [MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs](https://mlsanity.com/paper/2201.02534/) (2022)
- [A survey of dynamic graph neural networks](https://mlsanity.com/paper/2404.18211/) (2024)
- [Deep Generative Model for Periodic Graphs](https://mlsanity.com/paper/2201.11932/) (2022)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization](https://mlsanity.com/paper/2312.10988/) (2023)
- [How Expressive are Transformers in Spectral Domain for Graphs?](https://mlsanity.com/paper/2201.09332/) (2022)
- [Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks](https://mlsanity.com/paper/2308.06960/) (2023)
- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective](https://mlsanity.com/paper/2211.10929/) (2022)
- [Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation](https://mlsanity.com/paper/2210.09549/) (2022)
- [Hyperbolic Graph Diffusion Model](https://mlsanity.com/paper/2306.07618/) (2023)
- [DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization](https://mlsanity.com/paper/2408.04400/) (2024)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Persistent Homology and Graphs Representation Learning](https://mlsanity.com/paper/2102.12926/) (2021)
- [Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey](https://mlsanity.com/paper/2204.08226/) (2022)
- [Robust Graph Representation Learning via Predictive Coding](https://mlsanity.com/paper/2212.04656/) (2022)
- [Improving out-of-distribution generalization in graphs via hierarchical semantic environments](https://mlsanity.com/paper/2403.01773/) (2024)
- [Explainable Graph Neural Networks Under Fire](https://mlsanity.com/paper/2406.06417/) (2024)
- [Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks](https://mlsanity.com/paper/2112.13078/) (2021)
- [mathsfG2Retro as a Two-Step Graph Generative Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2206.04882/) (2022)
- [A Survey on Temporal Graph Representation Learning and Generative Modeling](https://mlsanity.com/paper/2208.12126/) (2022)
- [A Comprehensive Survey on Graph Summarization with Graph Neural Networks](https://mlsanity.com/paper/2302.06114/) (2023)
- [Independent Distribution Regularization for Private Graph Embedding](https://mlsanity.com/paper/2308.08360/) (2023)

## Abstract (arXiv)

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several state-of-the-art methods against small and large datasets and compare their performance. Finally, potential applications and future directions are presented.