# A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques

arXiv: https://arxiv.org/abs/2211.05594 · HTML: https://mlsanity.com/paper/2211.05594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders](https://mlsanity.com/paper/1802.03480/) (2018)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://mlsanity.com/paper/1805.11273/) (2018)
- [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://mlsanity.com/paper/1802.04407/) (2018)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts](https://mlsanity.com/paper/1905.02138/) (2019)
- [Supervised Dimensionality Reduction and Visualization using Centroid-encoder](https://mlsanity.com/paper/2002.11934/) (2020)
- [Deep Feature Learning for Graphs](https://mlsanity.com/paper/1704.08829/) (2017)
- [GraphTSNE: A Visualization Technique for Graph-Structured Data](https://mlsanity.com/paper/1904.06915/) (2019)
- [Unsupervised Network Embedding for Graph Visualization, Clustering and Classification](https://mlsanity.com/paper/1903.05980/) (2019)

## Abstract (arXiv)

Dimensionality reduction techniques map data represented on higher dimensions onto lower dimensions with varying degrees of information loss. Graph dimensionality reduction techniques adopt the same principle of providing latent representations of the graph structure with minor adaptations to the output representations along with the input data. There exist several cutting edge techniques that are efficient at generating embeddings from graph data and projecting them onto low dimensional latent spaces. Due to variations in the operational philosophy, the benefits of a particular graph dimensionality reduction technique might not prove advantageous to every scenario or rather every dataset. As a result, some techniques are efficient at representing the relationship between nodes at lower dimensions, while others are good at encapsulating the entire graph structure on low dimensional space. We present this survey to outline the benefits as well as problems associated with the existing graph dimensionality reduction techniques. We also attempted to connect the dots regarding the potential improvements to some of the techniques. This survey could be helpful for upcoming researchers interested in exploring the usage of graph representation learning to effectively produce low-dimensional graph embeddings with varying degrees of granularity.