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

ArXiv ID: 2508.04671 “View on arXiv”

Authors: Kundan Mukhia, SR Luwang, Md. Nurujjaman, Tanujit Chakraborty, Suman Saha, Chittaranjan Hens

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 ($γ\approx 2.3$), and adherence to TL with scaling coefficients ($β\approx 2.3$). In contrast, interactions involving SCs, especially SC-SC, exhibit sublinear scaling, unstable power-law exponents, and significantly fluctuating Taylor coefficients (variation in $β$ 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.

Keywords: Scaling Laws, ERC20 Tokens, Power Law Distributions, Temporal Taylor’s Law, Blockchain Analytics, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The mathematics is accessible, relying primarily on basic statistics like power-law exponents and Taylor’s law without complex derivations, placing it at low complexity. The paper demonstrates high empirical rigor with a large, real-world blockchain dataset (44 million transactions), temporal segmentation, and statistical analysis of distinct transaction types, making it data-heavy and ready for practical implementation.

  flowchart TD
    A["Research Goal:<br>Identify Universal Scaling Patterns<br>in ERC20 Token Networks"] --> B["Data Input:<br>44M+ ERC20 Token Transfers<br>July 2017 - March 2018"]
    B --> C["Methodology:<br>Classification by Address Type<br>EOA vs. SC & Temporal Segments"]
    C --> D["Computational Analysis:<br>Power Law & Temporal Taylor's Law"]
    D --> E["Key Finding 1:<br>EOA Activity<br>Stable Power Law (γ≈2.3)<br>Linear Scaling (β≈2.3)"]
    D --> F["Key Finding 2:<br>SC-Driven Activity<br>Heavy Tails (γ<2)<br>Bursty/Unstable (Δβ=0.51)"]
    E --> G["Outcome:<br>Universal Patterns Revealed<br>Distinguishing Human vs.<br>Automated Blockchain Behavior"]
    F --> G