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Volume 10 Issue 5
May  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Y. Zhou, X. Luo, and M. C. Zhou, “Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1105–1121, May 2023. doi: 10.1109/JAS.2023.123450
Citation: Y. Zhou, X. Luo, and M. C. Zhou, “Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1105–1121, May 2023. doi: 10.1109/JAS.2023.123450

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

doi: 10.1109/JAS.2023.123450
Funds:  This work was supported in part by the National Natural Science Foundation of China (62272078), the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2021-035A), and the Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications (BYJS202009)
More Information
  • Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding (CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure, thereby discovering desired patterns demonstrating involved users’ normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives, thereby promoting further research into this emerging and important field.

     

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    Highlights

    • Progress of Cryptocurrency Transaction Network Embedding (CTNE) methodology and algorithms from static to dynamic perspectives has been thoroughly reviewed
    • Typical evaluation metrics, along with the most commonly-adopted and publicly available CTNE datasets have been summarized
    • Reviewed typical CTNE methods have been validated on two large-scale datasets to provide practitioner with empirical guidance
    • The developing trends of CTNE methodology have been discussed

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