Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
Yunyi Xie, Jie Jin, Jian Zhang, Shanqing Yu, and Qi Xuan

TL;DR
This paper introduces a novel temporal-amount snapshot multigraph framework and a walk method for embedding Ethereum transaction networks, enhancing transaction tracking and network understanding.
Contribution
It proposes TASMG and TAW, new methods that incorporate temporal and amount features for improved transaction network embedding and link prediction in Ethereum.
Findings
TASMG effectively models Ethereum transaction data as a temporal-amount network.
TAW improves embedding quality by integrating temporal and amount information.
Experimental results show the framework's superiority in transaction tracking tasks.
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Advanced Graph Neural Networks · Complex Network Analysis Techniques
