T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis

• Main Authors: Dan Lin, Jiajing Wu, Qi Yuan, Zibin Zheng
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-06-01
• Series: Frontiers in Physics
• Subjects: network embedding; ethereum; machine learning; temporal network; transaction network
• Online Access: https://www.frontiersin.org/article/10.3389/fphy.2020.00204/full

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 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 a timestamp. We then 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 the multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.