# The Unreasonable Effectiveness of Address Clustering

arXiv: https://arxiv.org/abs/1605.06369 · HTML: https://mlsanity.com/paper/1605.06369/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2016.

## Built on by (most cited first, 8 of 8)

- [The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset](https://mlsanity.com/paper/2404.19109/) (2024)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks](https://mlsanity.com/paper/2104.02153/) (2021)
- [Pattern Analysis of Money Flow in the Bitcoin Blockchain](https://mlsanity.com/paper/2207.07315/) (2022)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Identifying Ransomware Actors in the Bitcoin Network](https://mlsanity.com/paper/2108.13807/) (2021)
- [Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain](https://mlsanity.com/paper/2307.08616/) (2023)
- [Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks](https://mlsanity.com/paper/2609.01942/) (2026)

## Abstract (arXiv)

Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality, and the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.