# Fast Sequence-Based Embedding with Diffusion Graphs

arXiv: https://arxiv.org/abs/2001.07463 · HTML: https://mlsanity.com/paper/2001.07463/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings](https://mlsanity.com/paper/2101.03091/) (2021)
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- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection](https://mlsanity.com/paper/2303.18138/) (2023)
- [Neural Embedding: Learning the Embedding of the Manifold of Physics Data](https://mlsanity.com/paper/2208.05484/) (2022)
- [MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy](https://mlsanity.com/paper/2110.15087/) (2021)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Persistent Homology and Graphs Representation Learning](https://mlsanity.com/paper/2102.12926/) (2021)
- [Network representation learning: A macro and micro view](https://mlsanity.com/paper/2111.10772/) (2021)
- [Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash](https://mlsanity.com/paper/2201.06811/) (2022)
- [TigerLily: Finding drug interactions in silico with the Graph](https://mlsanity.com/paper/2204.08206/) (2022)
- [Learning Graph Node Embeddings by Smooth Pair Sampling](https://mlsanity.com/paper/2501.12884/) (2025)
- [TopoEmbedX: A General Framework for Representation Learning on Topological Domains](https://mlsanity.com/paper/2609.37884/) (2026)

## Abstract (arXiv)

A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods.