  As the largest public blockchain-based platform supporting smart contracts,
Ethereum has accumulated a large number of user transaction records since its
debut in 2014. Analysis of Ethereum transaction records, however, is still
relatively unexplored till now. Modeling the transaction records as a static
simple graph, existing methods are unable to accurately characterize the
temporal and multiplex features of the edges. In this brief, we first model the
Ethereum transaction records as a complex network by incorporating time and
amount features of the transactions, and then design several flexible temporal
walk strategies for random-walk based graph representation of this large-scale
network. Experiments of temporal link prediction on real Ethereum data
demonstrate that temporal information and multiplicity characteristic of edges
are indispensable for accurate modeling and understanding of Ethereum
transaction networks.
