# BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis

arXiv: https://arxiv.org/abs/2204.05746 · HTML: https://mlsanity.com/paper/2204.05746/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 10 Apr 2022.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [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)
- [Ransomware Payments in the Bitcoin Ecosystem](https://mlsanity.com/paper/1804.04080/) (2018)
- [An Evaluation of Bitcoin Address Classification based on Transaction History Summarization](https://mlsanity.com/paper/1903.07994/) (2019)
- [Knowledge Discovery in Cryptocurrency Transactions: A Survey](https://mlsanity.com/paper/2010.01031/) (2020)
- [On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective](https://mlsanity.com/paper/1804.01341/) (2018)
- [Tracing Transactions Across Cryptocurrency Ledgers](https://mlsanity.com/paper/1810.12786/) (2018)
- [Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview](https://mlsanity.com/paper/2011.09318/) (2020)
- [Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem](https://mlsanity.com/paper/1908.01051/) (2019)
- [Complex Network Analysis of the Bitcoin Transaction Network](https://mlsanity.com/paper/2203.09026/) (2022)
- [A Deep Dive into Bitcoin Mining Pools: An Empirical Analysis of Mining Shares](https://mlsanity.com/paper/1905.05999/) (2019)
- [Cryptocurrencies Activity as a Complex Network: Analysis of Transactions Graphs](https://mlsanity.com/paper/2110.14765/) (2021)

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

- [Bitcoin Research with a Transaction Graph Dataset](https://mlsanity.com/paper/2411.10325/) (2024)
- [Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study](https://mlsanity.com/paper/2501.18158/) (2025)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction](https://mlsanity.com/paper/2409.18895/) (2024)
- [Leveraging Large Language Models to Bridge Cross-Domain Transparency in Stablecoins](https://mlsanity.com/paper/2512.02418/) (2025)

## Abstract (arXiv)

Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the underpinning ledgers, where any individual can access the record of a transaction record on the public ledger. In this paper, we build a dataset comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin addresses, 5 categories of indicators with 148 features, and 544,462 labeled data, which is the largest labeled Bitcoin address behavior dataset publicly available to our knowledge. We then use our proposed dataset on common machine learning models, namely: k-nearest neighbors algorithm, decision tree, random forest, multilayer perceptron, and XGBoost. The results show that the accuracy rates of these machine learning models for the multi-classification task on our proposed dataset are between 93.24% and 97.13%. We also analyze the proposed features and their relationships from the experiments, and propose a k-hop subgraph generation algorithm to extract a k-hop subgraph from the entire Bitcoin transaction graph constructed by the directed heterogeneous multigraph starting from a specific Bitcoin address node (e.g., a known transaction associated with a criminal investigation). Besides, we initially analyze the behavior patterns of different types of Bitcoin addresses according to the extracted features.