# GraphTSNE: A Visualization Technique for Graph-Structured Data

arXiv: https://arxiv.org/abs/1904.06915 · HTML: https://mlsanity.com/paper/1904.06915/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Deep Neural Network for DrawiNg Networks, (DNN)^2](https://mlsanity.com/paper/2108.03632/) (2021)
- [End-to-end AI framework for interpretable prediction of molecular and crystal properties](https://mlsanity.com/paper/2212.11317/) (2022)
- [Node Embeddings via Neighbor Embeddings](https://mlsanity.com/paper/2503.23822/) (2025)
- [Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images](https://mlsanity.com/paper/2202.09179/) (2022)
- [A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques](https://mlsanity.com/paper/2211.05594/) (2022)

## Abstract (arXiv)

We present GraphTSNE, a novel visualization technique for graph-structured data based on t-SNE. The growing interest in graph-structured data increases the importance of gaining human insight into such datasets by means of visualization. Among the most popular visualization techniques, classical t-SNE is not suitable on such datasets because it has no mechanism to make use of information from the graph structure. On the other hand, visualization techniques which operate on graphs, such as Laplacian Eigenmaps and tsNET, have no mechanism to make use of information from node features. Our proposed method GraphTSNE produces visualizations which account for both graph structure and node features. It is based on scalable and unsupervised training of a graph convolutional network on a modified t-SNE loss. By assembling a suite of evaluation metrics, we demonstrate that our method produces desirable visualizations on three benchmark datasets.