# Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain

arXiv: https://arxiv.org/abs/2306.07974 · HTML: https://mlsanity.com/paper/2306.07974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain](https://mlsanity.com/paper/1906.07852/) (2019)
- [Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods](https://mlsanity.com/paper/1611.03941/) (2016)
- [Ransomware Payments in the Bitcoin Ecosystem](https://mlsanity.com/paper/1804.04080/) (2018)
- [Cascading Machine Learning to Attack Bitcoin Anonymity](https://mlsanity.com/paper/1910.06560/) (2019)
- [On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective](https://mlsanity.com/paper/1804.01341/) (2018)
- [Blockchain Networks: Data Structures of Bitcoin, Monero, Zcash, Ethereum, Ripple and Iota](https://mlsanity.com/paper/2103.08712/) (2021)
- [Obfuscation in Bitcoin: Techniques and Politics](https://mlsanity.com/paper/1706.05432/) (2017)

## Abstract (arXiv)

The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets. These illegal activities often utilize Bitcoin as the preferred payment method. However, current tools for detecting illicit behavior either rely on a few heuristics and laborious data collection processes or employ computationally inefficient graph neural network (GNN) models that are challenging to interpret. To overcome the computational and interpretability limitations of existing techniques, we introduce an effective solution called Chainlet Orbits. This approach embeds Bitcoin addresses by leveraging their topological characteristics in transactions. By employing our innovative address embedding, we investigate e-crime in Bitcoin networks by focusing on distinctive substructures that arise from illicit behavior. The results of our node classification experiments demonstrate superior performance compared to state-of-the-art methods, including both topological and GNN-based approaches. Moreover, our approach enables the use of interpretable and explainable machine learning models in as little as 15 minutes for most days on the Bitcoin transaction network.