Demystifying Ethereum account diversity: observations, models and analysis

Abstract

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts. These massive accounts can be divided into diverse categories, such as miners, tokens, and exchanges, which is termed as account diversity in this paper. The benefit of investigating diversity are multi-fold, including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities. Unfortunately, the exploration of blockchain account diversity remains scarce. Even the most relevant studies, which focus on the deanonymization of the accounts on Bitcoin, can hardly be applied on Ethereum since their underlying protocols and user idioms are different. To this end, we present the first attempt to demystify the account diversity on Ethereum. The key observation is that different accounts exhibit diverse behavior patterns, leading us to propose the heuristics for classification as the premise. We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation (MLE). We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness. Furthermore, we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions. As for the former, we validate two sweeping statements reliably: (1) standalone miners are gradually replaced by the mining pools and cooperative miners; (2) transactions related to the mining pool and exchanges take up a large share of the total transactions. The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

Graphical abstract

Keywords

blockchain / Ethereum / classification / account diversity / network analysis

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Acknowledgements

This work was supported by Key-Area Research and Development Program of Guangdong Province (2020B0101090005) and the National Natural Science Foundation of China (Grant No. 62072197).

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