Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking

• URL: http://arxiv.org/abs/2102.08013v1
• Date: Tue, 16 Feb 2021 08:21:16 GMT
• Title: Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
• Authors: Yunyi Xie, Jie Jin, Jian Zhang, Shanqing Yu, and Qi Xuan
• Abstract summary: We study the problem of transaction tracking via link prediction, which provides a deeper understanding of transactions from a network perspective. Specifically, we introduce an embedding based link prediction framework that is composed of temporal-amount snapshot multigraph (TASMG) and present temporal-amount walk (TAW) By taking the realistic rules and features of transaction networks into consideration, we propose TASMG to model transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records.
• Score: 5.579169055801065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain. In order to better understand this emerging market and explore more efficient countermeasures for effective supervision, it is imperative to track transactions on blockchain-based systems. Due to the openness of Ethereum, we can easily access the publicly available transaction records, model them as a complex network, and further study the problem of transaction tracking via link prediction, which provides a deeper understanding of Ethereum transactions from a network perspective. Specifically, we introduce an embedding based link prediction framework that is composed of temporal-amount snapshot multigraph (TASMG) and present temporal-amount walk (TAW). By taking the realistic rules and features of transaction networks into consideration, we propose TASMG to model Ethereum transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records, which integrates temporal and amount information of the proposed network. Experimental results demonstrate the superiority of the proposed framework in learning more informative representations and could be an effective method for transaction tracking.

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