TY - JOUR
T1 - Unravelling Token Ecosystem of EOSIO Blockchain
AU - Jiang, Zigui
AU - Zheng, Weilin
AU - Liu, Bo
AU - Dai, Hong Ning
AU - Xie, Haoran
AU - Luo, Xiapu
AU - Zheng, Zibin
AU - Li, Qing
N1 - Funding information:
This work was supported by the Guangdong Basic and Applied Basic Research Foundation (2023A1515011336), the Faculty Research Grants (DB22B7, DB23B2, and DB24A4) of Lingnan University, Hong Kong, and the Hong Kong RGC Project (No. PolyU15224121). (Corresponding author: Hong-Ning Dai.)
Publisher Copyright:
© 2024 IEEE
PY - 2024/10
Y1 - 2024/10
N2 - Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a systematic investigation of the EOSIO token ecosystem by conducting a comprehensive graph analysis of the entire on-chain EOSIO data (nearly 135 million blocks). We construct token-creator graphs, token-contract creator graphs, token-holder graphs, and token-transfer graphs to characterize token creators, holders, and transfer activities. Through graph analysis, we have obtained many insightful findings and observed some abnormal trading patterns. Moreover, we propose a fake-token detection algorithm to identify tokens generated by fake users or fake transactions and analyze their corresponding manipulation behaviors. Evaluation results also demonstrate the effectiveness of our algorithm.
AB - Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a systematic investigation of the EOSIO token ecosystem by conducting a comprehensive graph analysis of the entire on-chain EOSIO data (nearly 135 million blocks). We construct token-creator graphs, token-contract creator graphs, token-holder graphs, and token-transfer graphs to characterize token creators, holders, and transfer activities. Through graph analysis, we have obtained many insightful findings and observed some abnormal trading patterns. Moreover, we propose a fake-token detection algorithm to identify tokens generated by fake users or fake transactions and analyze their corresponding manipulation behaviors. Evaluation results also demonstrate the effectiveness of our algorithm.
KW - Blockchain
KW - EOSIO
KW - fake-token detection
KW - graph analysis
KW - token
UR - http://www.scopus.com/inward/record.url?scp=85189500848&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2024.3378381
DO - 10.1109/TKDE.2024.3378381
M3 - Journal article
AN - SCOPUS:85189500848
SN - 1041-4347
VL - 36
SP - 5423
EP - 5439
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 10
ER -