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.

近年来，图嵌入技术已广泛用于各种网络的分析中，但是大多数现有的嵌入方法都忽略了网络动力学和边缘的多样性，因此很难准确地描述交易网络的详细特征。以太坊是一个基于区块链的平台，支持智能合约。区块链的开放性使以太坊上的交易数据完全公开，也为交易网络分析带来了前所未有的机会。通过考虑现实的交易网络规则和功能，我们首先将以太坊交易网络建模为时间加权多重图（TWMDG），其中每个节点都是唯一的以太坊账户，每个边代表按金额加权并分配时间戳的交易。然后，我们通过结合边缘的时间信息和加权信息来定义时间加权多重图嵌入（T-EDGE）问题，目的是捕获动态交易网络的更全面的属性。为了评估所提出的嵌入方法的有效性，我们对从以太坊收集的真实交易数据进行了节点分类实验。实验结果表明，T-EDGE的性能优于基线嵌入方法，这表明与时间相关的步行和边缘的多重性对信息灵敏且对时间敏感的交易网络至关重要。目的是捕获动态交易网络的更全面的属性。为了评估所提出的嵌入方法的有效性，我们对从以太坊收集的真实交易数据进行了节点分类实验。实验结果表明，T-EDGE的性能优于基线嵌入方法，这表明与时间相关的步行和边缘的多重性对信息灵敏且对时间敏感的交易网络至关重要。目的是捕获动态交易网络的更全面的属性。为了评估所提出的嵌入方法的有效性，我们对从以太坊收集的真实交易数据进行了节点分类实验。实验结果表明，T-EDGE的性能优于基线嵌入方法，这表明与时间相关的步行和边缘的多重性对信息灵敏且对时间敏感的交易网络至关重要。