  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.
