# Force2Vec: Parallel force-directed graph embedding

arXiv: https://arxiv.org/abs/2009.10035 · HTML: https://mlsanity.com/paper/2009.10035/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Text characterization based on recurrence networks](https://mlsanity.com/paper/2201.06665/) (2022)
- [Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy](https://mlsanity.com/paper/2208.03559/) (2022)
- [A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods](https://mlsanity.com/paper/2112.10372/) (2021)
- [Force-directed graph embedding with hops distance](https://mlsanity.com/paper/2309.05865/) (2023)
- [MarkovGNN: Graph Neural Networks on Markov Diffusion](https://mlsanity.com/paper/2202.02470/) (2022)
- [Graph Spring Neural ODEs for Link Sign Prediction](https://mlsanity.com/paper/2412.12916/) (2024)
- [SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations](https://mlsanity.com/paper/2502.16949/) (2025)

## Abstract (arXiv)

A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. While graph embedding is fundamentally related to graph visualization, prior work did not exploit this connection explicitly. We develop Force2Vec that uses force-directed graph layout models in a graph embedding setting with an aim to excel in both machine learning (ML) and visualization tasks. We make Force2Vec highly parallel by mapping its core computations to linear algebra and utilizing multiple levels of parallelism available in modern processors. The resultant algorithm is an order of magnitude faster than existing methods (43x faster than DeepWalk, on average) and can generate embeddings from graphs with billions of edges in a few hours. In comparison to existing methods, Force2Vec is better in graph visualization and performs comparably or better in ML tasks such as link prediction, node classification, and clustering. Source code is available at https://github.com/HipGraph/Force2Vec.