# Do the rich get richer? An empirical analysis of the BitCoin transaction network

arXiv: https://arxiv.org/abs/1308.3892 · HTML: https://mlsanity.com/paper/1308.3892/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2013.

## Built on by (most cited first, 7 of 7)

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- [Social Choice Around the Block: On the Computational Social Choice of Blockchain](https://mlsanity.com/paper/2203.07777/) (2022)
- [DAO-enabled decentralized physical AI: A new paradigm for human-machine collaboration](https://mlsanity.com/paper/2605.04522/) (2026)
- [Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19](https://mlsanity.com/paper/2101.00576/) (2021)
- [Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain](https://mlsanity.com/paper/2307.08616/) (2023)
- [CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion](https://mlsanity.com/paper/2604.16411/) (2026)
- [Directly Optimizing Mean Demographic Parity for Nonlinear Regression](https://mlsanity.com/paper/2607.05098/) (2026)

## Abstract (arXiv)

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze BitCoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions, and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling relation between the degree and wealth associated to individual nodes.