Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview

TL;DR

This survey reviews how network analysis techniques are applied to cryptocurrency transaction data, highlighting methods, findings, and future challenges in understanding blockchain-based financial activities.

Contribution

It systematically summarizes existing research on cryptocurrency transaction networks, covering modeling, profiling, detection, and future research directions.

Findings

Network modeling techniques for cryptocurrency transactions.

Identification of key patterns and anomalies in transaction networks.

Challenges in privacy, scalability, and real-time analysis.

Abstract

As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in the blocks. These transaction records containing rich information and complete traces of financial activities are publicly accessible, thus providing researchers with unprecedented opportunities for data mining and knowledge discovery in this area. Networks are a general language for describing interacting systems in the real world, and a considerable part of existing work on cryptocurrency transactions is studied from a network perspective. This survey aims to analyze and summarize the existing literature on analyzing and understanding cryptocurrency transactions from a network perspective. Aiming to provide a systematic guideline for researchers and engineers, we present the background information of cryptocurrency transaction network analysis and review existing research in terms of three aspects, i.e., network modeling, network profiling, and network-based detection. For each aspect, we introduce the research issues, summarize the methods, and discuss the results and findings given in the literature. Furthermore, we present the main challenges and several future directions in this area.