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EOS.IO blockchain data analysis

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Abstract

Cryptocurrency is a relatively mature application of blockchain technology. The openness of transaction records provides researchers with the opportunity to analyse and compare various cryptocurrencies. The EOS public chain based on EOS.IO supports millions of transactions per second, with billions of transactions, and provides data analysts with a large quantity of analysable transaction data. Combined with the Ethereum platform data of the same period, this paper focuses on the transaction data in the EOS.IO blockchain and analyses the data in the Ethereum and EOS.IO chains from a complex network perspective. By constructing cumulative networks and time-slicing methods, constructing transaction networks of different scales, and dynamically analysing the laws of transaction network changes over time, we find that many transactions, such as transaction volume and transaction relationships, exhibit heavy-tail characteristics and conform to a power-law distribution. In particular, with the change in time and the growth in network scale, the power-law distribution is time-invariant. Our research can verify and predict the progress of blockchain development. Through graph analysis, we also obtained some other observations and discovered some interesting mathematical characteristics that explain the actual interactions that occurred on the blockchain.

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Acknowledgements

This work was partially supported by the Key Research and Development Program of Shandong Province (2017GGX10142, 2019GNC106027, 2019JZZY010134), and the Natural Science Foundation of Shandong Province (ZR2020MF058).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250000, Shandong, China

    Wanshui Song, Linbo Zhai & Shanyun Huang

  2. School of Information Science and Engineering, Linyi University, Linyi, 276002, Shandong, China

    Wanshui Song, Wenyin Zhang, Luanqi Liu, Jiuru Wang, Shanyun Huang & Bei Li

  3. School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266000, Shandong, China

    Bei Li

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  1. Wanshui Song
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Contributions

WSS, WYZ, and LLZ contributed to conceptualization; WSS, WYZ and LLZ contributed to methodology; WSS, LQL, and WYZ contributed to validation; WSS helped in formal analysis; LQL, and SYH helped in data curation; WSS and LQL writing—original draft preparation; JRW and BL contributed to writing—review and editing; WSS and LQL contributed to visualization. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Wenyin Zhang or Linbo Zhai.

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Song, W., Zhang, W., Zhai, L. et al. EOS.IO blockchain data analysis. J Supercomput 78, 5974–6005 (2022). https://doi.org/10.1007/s11227-021-04090-y

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