标题： 区块链中的通用模式：对ERC20代币网络中外部账户和智能合约的分析 显示英文标题

标题： Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks

摘要： 缩放定律为理解去中心化系统中的复杂交易行为提供了强大的视角。 本研究通过分析2017年7月至2018年3月（9个月期间）之间超过4400万次代币转账，揭示了以太坊区块链上ERC20代币的交易动态中的独特统计特征。 交易被分为四种类型：EOA--EOA、EOA--SC、SC-EOA和SC-SC，根据交互地址是外部账户（EOAs）还是智能合约（SCs）进行分类，并在三个相等的时间段（每个3个月）内进行分析。 为了识别普遍的统计模式，我们研究了两种典型的缩放定律：幂律分布和时间泰勒定律（TL）。 由EOA驱动的交易表现出一致的统计行为，包括交易量与唯一合作伙伴之间的近线性关系，以及稳定的幂律指数（$\gamma \approx 2.3$），并且遵循TL，其缩放系数（$\beta \approx 2.3$）。 相比之下，涉及SC的交互，尤其是SC-SC，表现出次线性缩放、不稳定的幂律指数以及显著波动的泰勒系数（$\beta$的变化范围为$\Delta\beta = 0.51$）。 此外，由SC驱动的活动表现出更重尾的分布（$\gamma < 2$），表明具有突发性和算法驱动的活动。 这些发现揭示了区块链生态系统中人为控制与自动化交易行为之间的特征差异。 通过结合复杂系统理论和区块链数据分析来揭示普遍的缩放行为，这项工作提供了一个有原则的框架，以理解去中心化金融系统的潜在机制。 显示英文摘要

摘要： Scaling laws offer a powerful lens to understand complex transactional behaviors in decentralized systems. This study reveals distinctive statistical signatures in the transactional dynamics of ERC20 tokens on the Ethereum blockchain by examining over 44 million token transfers between July 2017 and March 2018 (9-month period). Transactions are categorized into four types: EOA--EOA, EOA--SC, SC-EOA, and SC-SC based on whether the interacting addresses are Externally Owned Accounts (EOAs) or Smart Contracts (SCs), and analyzed across three equal periods (each of 3 months). To identify universal statistical patterns, we investigate the presence of two canonical scaling laws: power law distributions and temporal Taylor's law (TL). EOA-driven transactions exhibit consistent statistical behavior, including a near-linear relationship between trade volume and unique partners with stable power law exponents ($\gamma \approx 2.3$), and adherence to TL with scaling coefficients ($\beta \approx 2.3$). In contrast, interactions involving SCs, especially SC-SC, exhibit sublinear scaling, unstable power-law exponents, and significantly fluctuating Taylor coefficients (variation in $\beta$ to be $\Delta\beta = 0.51$). Moreover, SC-driven activity displays heavier-tailed distributions ($\gamma < 2$), indicating bursty and algorithm-driven activity. These findings reveal the characteristic differences between human-controlled and automated transaction behaviors in blockchain ecosystems. By uncovering universal scaling behaviors through the integration of complex systems theory and blockchain data analytics, this work provides a principled framework for understanding the underlying mechanisms of decentralized financial systems.