Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29–31, 2024, Wuhan, China

Research Article

Research on the Important Accounts Identification in Blockchain Trading Networks

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347430,
        author={Min  Zhang and Yongsheng  Zhao and Zesan  Liu and Jian  Zhang and Yunxi  Fu and Zhenya  Wang},
        title={Research on the Important Accounts Identification in Blockchain Trading Networks},
        proceedings={Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2024},
        month={6},
        keywords={blockchain;  complex network;  critical node; network security},
        doi={10.4108/eai.29-3-2024.2347430}
    }
    
  • Min Zhang
    Yongsheng Zhao
    Zesan Liu
    Jian Zhang
    Yunxi Fu
    Zhenya Wang
    Year: 2024
    Research on the Important Accounts Identification in Blockchain Trading Networks
    ICBBEM
    EAI
    DOI: 10.4108/eai.29-3-2024.2347430
Min Zhang1,*, Yongsheng Zhao1, Zesan Liu1, Jian Zhang2, Yunxi Fu1, Zhenya Wang1
  • 1: State Grid Information & Telecommunication Group CO., LTD
  • 2: State Grid Tianjin Electric Power Company
*Contact email: mz2016bupt@163.com

Abstract

Important accounts play an important role in guaranteeing the security and stability of blockchain trading networks, given that attacking them can increase the risk of account theft and disrupt the trade order. Consequently, the identification of critical accounts within blockchain trading networks holds paramount significance. However, previous research usually focuses on individual account features while neglecting the impacts of neighbors, leading to biased assessments and inaccurate ranking lists. To overcome these limitations, this paper proposes the NDL algorithm to identify critical accounts in the blockchain trading networks based on complex network methods. Specifically, NDL utilizes degree centrality to compute the attributes of an account itself, and employs the shortest paths to calculate the attributes of its neighbors. By comprehensively considering the influence of accounts and neighbors, NDL effectively distinguishes their importance. Besides, the Susceptible-Infectious-Recovered (SIR) model is employed to estimate the transmission potential of accounts. In addition, Kendall’s tau correlation coefficient and monotonicity index are employed to assess the effectiveness and distinguishability of NDL. After conducting thorough experiments on four datasets, the findings demonstrate that NDL outperforms six baseline methods.