DOI: 10.1371/journal.pone.0358972 ISSN: 1932-6203

Statistical patterns in the Ethereum blockchain: Analysis of EOAs and smart contracts in ERC20 token network

Kundan Mukhia, S. R. Luwang, Md. Nurujjaman, Tanujit Chakraborty, Suman Saha, Chittaranjan Hens
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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 81 million token transfers across two independent time windows: July 2017 to March 2018 and December 2019 to February 2020. 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 four equal periods (each of 3 months). To characterize and identify specific 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 ≈ 2.3
), and adherence to TL with scaling coefficients (
β ≈ 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
Δ β = 0.51
). Moreover, SC-driven activity displays heavier-tailed distributions (
γ < 2 ), indicating bursty and protocol-level activity. The gas fee analysis further supports the difference between EOA and SC, with EOA-SC transactions incurring much higher gas fees, consistent with the greater computational complexity of smart contract execution and reinforcing the structural differences between EOA-driven activity and SC involvement. These findings reveal the characteristic differences between EOA-driven and SC–driven transaction behaviors in blockchain ecosystems. By uncovering 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.

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