Know Your Transactions: Real-time and Generic Transaction Semantic Representation on Blockchain & Web3 Ecosystem
Zhiying Wu, Jieli Liu, Jiajing Wu, Zibin Zheng, Xiapu Luo, Ting Chen
Abstract
Web3, based on blockchain technology, is the evolving next generation Internet of value. Massive active applications on Web3, e.g. DeFi and NFT, usually rely on blockchain transactions to achieve value transfer as well as complex and diverse custom logic and intentions. Various risky or illegal behaviors such as financial fraud, hacking, money laundering are currently rampant in the blockchain ecosystem, and it is thus important to understand the intent behind the pseudonymous transactions. To reveal the intent of transactions, much effort has been devoted to extracting some particular transaction semantics through specific expert experiences. However, the limitations of existing methods in terms of effectiveness and generalization make it difficult to extract diverse transaction semantics in the rapidly growing and evolving Web3 ecosystem. In this paper, we propose the Motif-based Transaction Semantics representation method (MoTS), which can capture the transaction semantic information in the real-time transaction data workflow. To the best of our knowledge, MoTS is the first general semantic extraction method in Web3 blockchain ecosystem. Experimental results show that MoTS can effectively distinguish different transaction semantics in real-time, and can be used for various downstream tasks, giving new insights to understand the Web3 blockchain ecosystem. Our codes are available at https://github.com/wuzhy1ng/MoTS.
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