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

2025-08-06

AI Summary

Distinguishing human (externally owned account, EOA) from automated (smart contract, SC) behavior in Ethereum ERC-20 transactions remains a fundamental challenge in blockchain behavioral analysis. Method: Leveraging large-scale on-chain temporal data, we propose a quantitative framework integrating power-law distribution analysis with temporal Taylor’s law modeling to systematically characterize interaction patterns across four address types. Contribution/Results: We discover, for the first time, that EOA-dominated transactions exhibit stable power-law scaling (γ ≈ β ≈ 2.3), whereas SC-to-SC interactions display pronounced heavy-tailedness and dynamic scaling fluctuations (Δβ = 0.51), confirming their bursty and non-stationary nature. This statistically grounded distinction provides the first empirically verifiable criterion for disentangling human decision-making from algorithm-driven activity in decentralized finance. It advances blockchain behavioral modeling from qualitative description toward rigorous, quantifiable differentiation—enabling more accurate anomaly detection, protocol design, and regulatory oversight.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Web Mining and Content Analysis: Content-based information diffusion
• Security and Privacy: Blockchains and distributed ledgers

Abstract

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 ($γapprox 2.3$), and adherence to TL with scaling coefficients ($βapprox 2.3$). In contrast, interactions involving SCs, especially SC-SC, exhibit sublinear scaling, unstable power-law exponents, and significantly fluctuating Taylor coefficients (variation in $β$ to be $Δβ= 0.51$). Moreover, SC-driven activity displays heavier-tailed distributions ($γ< 2$), 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.

Problem

• Analyze ERC20 token transactional dynamics on Ethereum blockchain
• Identify scaling laws in EOA and SC transaction types
• Compare human-controlled vs automated transaction behaviors

Innovation

• Analyzing ERC20 token transfers using scaling laws
• Categorizing transactions into four distinct types
• Identifying power law and Taylor's law patterns