  Recently, graph embedding techniques have been widely used in the analysis of
various networks, but most of the existing embedding methods omit the network
dynamics and the multiplicity of edges, so it is difficult to accurately
describe the detailed characteristics of the transaction networks. Ethereum is
a blockchain-based platform supporting smart contracts. The open nature of
blockchain makes the transaction data on Ethereum completely public, and also
brings unprecedented opportunities for the transaction network analysis. By
taking the realistic rules and features of transaction networks into
consideration, we first model the Ethereum transaction network as a Temporal
Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and
each edge represents a transaction weighted by amount and assigned with
timestamp. Then we define the problem of Temporal Weighted Multidigraph
Embedding (T-EDGE) by incorporating both temporal and weighted information of
the edges, the purpose being to capture more comprehensive properties of
dynamic transaction networks. To evaluate the effectiveness of the proposed
embedding method, we conduct experiments of node classification on real-world
transaction data collected from Ethereum. Experimental results demonstrate that
T-EDGE outperforms baseline embedding methods, indicating that time-dependent
walks and multiplicity characteristic of edges are informative and essential
for time-sensitive transaction networks.
