Temporal Analysis of the Entire Ethereum Blockchain Network

TLDR

Ethereum, with a market capitalization exceeding 42 billion USD, is the largest public blockchain, yet most research has focused on static graph models, leaving its temporal evolution largely unexplored. This study investigates Ethereum’s interaction networks from a temporal graph perspective. We analyze growth rates, model four Ethereum networks, and examine high‑degree vertex lifespans and update rates, then employ temporal graph features and machine‑learning models to detect anomalies and forecast community survival. Our analysis identifies anomalies in global network properties and successfully predicts the survival of network communities in future months.

Abstract

With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.