Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking

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 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.