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

Yue Zhou, Xin Luo, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1105-1121
Number of pages17
JournalIEEE/CAA Journal of Automatica Sinica
Volume10
Issue number5
DOIs
StatePublished - May 1 2023

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Control and Optimization
  • Artificial Intelligence

Keywords

  • Big data analysis
  • cryptocurrency transaction network embedding (CTNE)
  • dynamic network
  • network embedding
  • network representation
  • static network

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