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

作     者：Yue Zhou Xin Luo MengChu Zhou Yue Zhou;Xin Luo;MengChu Zhou

作者机构：the School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing SchoolUniversity of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information ScienceSouthwest UniversityChongqing 400715China the School of Information and Electronic EngineeringZhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2023年第10卷第5期

页      面：1105-1121页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the National Natural Science Foundation of China (62272078) the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2021-035A) the Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications (BYJS202009) 

主　　题：Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network 

摘      要：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.