Abstract
Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross-Innovation-Lab/BitcoinAddressClustering.
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