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.

区块链平台以太坊由于其提供基于智能合约的众多服务的强大潜力，已涉及数百万个账户。这些海量账户可以分为矿工、代币、交易所等不同类别，本文称为账户多样性。调查多样性的好处是多方面的，包括更深入地了解以太坊生态系统并开启跟踪某些异常活动的可能性。不幸的是，对区块链账户多样性的探索仍然很少。即使是关注比特币账户去匿名化的最相关研究也很难应用于以太坊，因为它们的底层协议和用户习惯用法不同。为此，我们首次尝试揭开以太坊账户多样性的神秘面纱。关键的观察是不同的账户表现出不同的行为模式，导致我们提出分类的启发式作为前提。然后我们通过统计学习模型提高分类的覆盖率最大似然估计(MLE)。我们通过广泛的努力来收集真实世界的数据，以评估我们提出的方法并展示其有效性。此外，我们对以太坊生态系统的动态演变进行了深入分析，揭示了异常的套利行为。对于前者，我们可靠地验证了两个笼统的说法：（1）独立矿工逐渐被矿池和合作矿工取代；(2) 矿池和交易所相关的交易占总交易的很大一部分。后一种分析表明，有大量套利交易将硬币从一个交易所转移到另一个交易所以产生价格差异。