https://doi.org/10.1140/epjds/s13688-021-00276-9
Regular Article
Characterizing key agents in the cryptocurrency economy through blockchain transaction analysis
1
Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, 18 Tat Hong Avenue, Kowloon, Hong Kong SAR, China
2
School of Computer Science and Engineering, Southeast University, 2 Dongnandaxue Road, 211189, Nanjing, China
Received:
5
July
2020
Accepted:
21
April
2021
Published online:
1
May
2021
The cryptocurrency economy provides a comprehensive digital trace of human economic behavior: almost all cryptocurrency users’ activities are faithfully recorded in transactions on public blockchains. However, the user identifiers in the transaction records, i.e., blockchain addresses, are anonymous. That is, they cannot be associated with any real “off-chain” identify of actual users. Nonetheless, identifying the economic roles of the addresses from their past behaviors is still feasible. This paper analyzes Ethereum token transactions, characterizes key economic agents’ behavior from their transaction patterns, and explores their identifiability through interpretable machine learning models. Specifically, six types of most active economic agents are considered, including centralized cryptocurrency exchanges, decentralized exchanges, cryptocurrency wallets, token issuers, airdrop services, and gaming services. Transaction patterns such as trading volume, transaction tempo, and structural properties of transaction networks are defined for individual blockchain addresses. The results showed that cryptocurrency exchanges and online wallets have signature behavior patterns and hence can be accurately distinguished from other agents. Token issuers, airdrop services, and gaming services can sometimes be confused. Moreover, transaction networks’ features provide the richest information in the economic agent’s identification.
Key words: Cryptocurrency / Ethereum / Deanonymization / Network analysis / Machine learning
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-021-00276-9.
© The Author(s) 2021
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