期刊
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
卷 9, 期 2, 页码 559-570出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2021.3093384
关键词
Blockchain; Feature extraction; Cryptocurrency; Smart contracts; Prediction algorithms; Microscopy; Data mining; Complex network; cryptocurrency; Ethereum; network evolution; transaction relationships
资金
- National Key Research and Development Program of China [2020YFB1006005]
- National Natural Science Foundation of China [61973325, 62032025]
- Natural Science Foundations of Guangdong Province [2021A1515011661]
Researchers collected Ethereum transaction data and built network models, finding that the local and microscopic network structures are star-shaped, and the transaction frequency of addresses has a significant impact on the evolution of Ethereum transaction relationships. The first-layer nodes of microstructures play a dominant role in network evolution. The degree of addresses is effective in predicting the direction of new transactions.
Much of the current research in Ethereum transaction records focuses on the statistical analysis and measurements of existing data; however, the evolution mechanism of Ethereum transactions is an important, yet seldom discussed issue. In this work, we first collect the transaction data of Ethereum and build network models from a microlevel view and then use a link-prediction-based framework to quantify the impact of network characteristics on Ethereum evolution. Next, we explore the graph structure properties and the driving factors of newly generated transaction relationships. Experimental results show that the local and microscopic structure of Ethereum networks is star-shaped, and the transaction frequency of addresses has a great impact on the evolution of Ethereum transaction relationships. First-layer nodes of microstructures dominate the network evolution. Moreover, the degree of addresses is an effective basis for predicting the direction of new transactions. Potential further studies on Ethereum transaction link prediction are discussed, for example, the label effect of center addresses.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据