Temporal Analysis of Transaction Ego Networks with Different Labels on Ethereum

TL;DR

This paper analyzes the dynamic transaction patterns of different labeled Ethereum accounts using ego networks, revealing significant differences in their network features and transaction behaviors.

Contribution

It introduces the first dynamic analysis of Ethereum labeled accounts through transaction ego networks, highlighting temporal interaction patterns.

Findings

Different account types show distinct network feature patterns.

Temporal analysis reveals significant behavioral differences.

Ego networks effectively characterize transaction dynamics.

Abstract

Due to the widespread use of smart contracts, Ethereum has become the second-largest blockchain platform after Bitcoin. Many different types of Ethereum accounts (ICO, Mining, Gambling, etc.) also have quite active trading activities on Ethereum. Studying the transaction records of these specific Ethereum accounts is very important for understanding their particular transaction characteristics, and further labeling the pseudonymous accounts. However, traditional methods are generally based on static and global transaction networks to conduct research, ignoring useful information about dynamic changes. Our work chooses six kinds of important account labels, and builds ego networks for each kind of Ethereum account. We focus on the interaction between the target node and neighbor nodes with temporal analysis. Experiments show that there is a significant difference between various types of accounts in terms of several network features, helping us better understand their transaction patterns. To the best of our knowledge, this is the first work to analyze the dynamic characteristics of Ethereum labeled accounts from the perspective of transaction ego networks.