TY - JOUR
T1 - Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives
T2 - An Overview
AU - Zhou, Yue
AU - Luo, Xin
AU - Zhou, Meng Chu
N1 - Funding Information:
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).
Publisher Copyright:
© 2014 Chinese Association of Automation.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - 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.
AB - 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.
KW - Big data analysis
KW - cryptocurrency transaction network embedding (CTNE)
KW - dynamic network
KW - network embedding
KW - network representation
KW - static network
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U2 - 10.1109/JAS.2023.123450
DO - 10.1109/JAS.2023.123450
M3 - Article
AN - SCOPUS:85149412361
SN - 2329-9266
VL - 10
SP - 1105
EP - 1121
JO - IEEE/CAA Journal of Automatica Sinica
JF - IEEE/CAA Journal of Automatica Sinica
IS - 5
ER -