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.

本研究利用时间和金额特征，将以太坊交易记录建模成复杂网络，通过设计多种灵活的基于随机游走的图表征策略，证明了边的时间和多样性特征对于准确建模和理解以太坊交易网络是不可或缺的

利用复杂网络方法对以太坊交易记录进行建模和理解

Modeling and Understanding Ethereum Transaction Records via a Complex  Network Approach

Recently, blockchain technology has become a topic in the spotlight but also
a hotbed of various cybercrimes. Among them, phishing scams on blockchain have
been found making a notable amount of money, thus emerging as a serious threat
to the trading security of the blockchain ecosystem. In order to create a
favorable environment for investment, an effective method for detecting
phishing scams is urgently needed in the blockchain ecosystem. To this end,
this paper proposes an approach to detect phishing scams on Ethereum by mining
its transaction records. Specifically, we first crawl the labeled phishing
addresses from two authorized websites and reconstruct the transaction network
according to the collected transaction records. Then, by taking the transaction
amount and timestamp into consideration, we propose a novel network embedding
algorithm called trans2vec to extract the features of the addresses for
subsequent phishing identification. Finally, we adopt the oneclass support
vector machine (SVM) to classify the nodes into normal and phishing ones.
Experimental results demonstrate that the phishing detection method works
effectively on Ethereum, and indicate the efficacy of trans2vec over existing
state-of-the-art algorithms on feature extraction for transaction networks.
This work is the first investigation on phishing detection on Ethereum via
network embedding and provides insights into how features of large-scale
transaction networks can be embedded.

通过交易记录的信息提取和网络嵌入技术，本文针对以太坊网络上的网络钓鱼现象提出了一种检测方法，并证明其在以太坊上的有效性和交易网络特征提取算法的优越性。