# Pattern Analysis of Money Flow in the Bitcoin Blockchain

arXiv: https://arxiv.org/abs/2207.07315 · HTML: https://mlsanity.com/paper/2207.07315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 16 of 16)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Anonymous Walk Embeddings](https://mlsanity.com/paper/1805.11921/) (2018)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018)
- [BlockSci: Design and applications of a blockchain analysis platform](https://mlsanity.com/paper/1709.02489/) (2017)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)
- [An Evaluation of Bitcoin Address Classification based on Transaction History Summarization](https://mlsanity.com/paper/1903.07994/) (2019)
- [Cascading Machine Learning to Attack Bitcoin Anonymity](https://mlsanity.com/paper/1910.06560/) (2019)
- [Characterizing Entities in the Bitcoin Blockchain](https://mlsanity.com/paper/1810.11956/) (2018)
- [When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies](https://mlsanity.com/paper/1708.04748/) (2017)
- [Tracking bitcoin users activity using community detection on a network of weak signals](https://mlsanity.com/paper/1710.08158/) (2017)
- [A Deep Dive into Bitcoin Mining Pools: An Empirical Analysis of Mining Shares](https://mlsanity.com/paper/1905.05999/) (2019)
- [Learning distributed representations of graphs with Geo2DR](https://mlsanity.com/paper/2003.05926/) (2020)
- [Tendrils of Crime: Visualizing the Diffusion of Stolen Bitcoins](https://mlsanity.com/paper/1901.01769/) (2019)

## Built on by (most cited first, 2 of 2)

- [Bitcoin Research with a Transaction Graph Dataset](https://mlsanity.com/paper/2411.10325/) (2024)
- [A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems](https://mlsanity.com/paper/2603.16080/) (2026)

## Abstract (arXiv)

Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows --dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns