# Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain

arXiv: https://arxiv.org/abs/2307.08616 · HTML: https://mlsanity.com/paper/2307.08616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)
- [Do the rich get richer? An empirical analysis of the BitCoin transaction network](https://mlsanity.com/paper/1308.3892/) (2013)
- [Complex Network Analysis of the Bitcoin Transaction Network](https://mlsanity.com/paper/2203.09026/) (2022)
- [Identifying Ransomware Actors in the Bitcoin Network](https://mlsanity.com/paper/2108.13807/) (2021)

## Abstract (arXiv)

We study the real economic activity in the Bitcoin blockchain that involves transactions from/to retail users rather than between organizations such as marketplaces, exchanges, or other services. We first introduce a heuristic method to classify Bitcoin players into three main categories: Frequent Receivers (FR), Neighbors of FR, and Others. We show that most real transactions involve Frequent Receivers, representing a small fraction of the total value exchanged according to the blockchain, but a significant fraction of all payments, raising concerns about the centralization of the Bitcoin ecosystem. We also conduct a weekly pattern analysis of activity, providing insights into the geographical location of Bitcoin users and allowing us to quantify the bias of a well-known dataset for actor identification.