[Paper Review] Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview

Jiajing Wu, Jieli Liu+2 more|arXiv (Cornell University)|Nov 18, 2020

Blockchain Technology Applications and SecurityComputer Science178 references181 citations

TL;DR

This paper provides a comprehensive survey of cryptocurrency transaction analysis from a network perspective, categorizing existing research into network modeling, profiling, and detection. It synthesizes methods for constructing transaction networks, analyzing structural and temporal properties, and detecting anomalies, offering a systematic framework for researchers in blockchain analytics and graph mining.

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.

Motivation & Objective

• To provide a systematic, comprehensive review of research on cryptocurrency transaction networks from a network science perspective.
• To categorize and analyze existing literature into three core research areas: network modeling, network profiling, and network-based detection.
• To identify key challenges and future research directions in analyzing large-scale, dynamic, and partially observable transaction networks.
• To serve as a foundational reference for researchers and engineers working on blockchain data mining, financial forensics, and graph-based analysis of decentralized systems.

Proposed method

• Categorizes network modeling techniques based on node and edge semantics (e.g., addresses, transactions, smart contracts).
• Reviews network profiling methods focusing on structural properties (e.g., power-law degree distribution), temporal evolution, and market effects.
• Analyzes network-based detection techniques for identifying money laundering, fraud, and other illicit activities using graph-based anomaly detection.
• Employs systematic literature review methodology using keywords like 'cryptocurrency', 'transaction', 'network', 'Bitcoin', and 'Ethereum' across journals, conferences, and preprints (2009–2020).
• Integrates findings from empirical studies, analytical models, and real-world data to assess network behavior and dynamics.
• Proposes future research directions including online learning, link prediction for incomplete networks, and audit frameworks for privacy-enhanced blockchains.

Experimental results

Research questions

• RQ1 How can cryptocurrency transaction data be effectively modeled as complex networks using different node and edge semantics?
• RQ2 What are the key structural and temporal properties of cryptocurrency transaction networks, and how do they evolve over time?
• RQ3 What network-based techniques can be used to detect anomalies, money laundering, or other illicit behaviors in blockchain systems?
• RQ4 What are the main challenges in analyzing large-scale, dynamic, and partially observable transaction networks?
• RQ5 How can emerging blockchain technologies (e.g., Lightning Network, sharding) affect the completeness and accuracy of network-based analysis?

Key findings

• Cryptocurrency transaction networks are the largest publicly accessible real-world networks, with hundreds of millions of transaction records in systems like Bitcoin and Ethereum.
• Transaction networks exhibit scale-free and small-world properties, with power-law degree distributions and high clustering, indicating a core-periphery structure.
• Temporal analysis reveals bursty transaction patterns and evolving network topologies, with significant implications for market dynamics and system robustness.
• Network-based detection methods, especially those using community detection and centrality measures, have shown high effectiveness in identifying suspicious clusters and money mule accounts.
• Incomplete data due to off-chain solutions (e.g., Lightning Network) and privacy-enhancing techniques (e.g., mixing services) pose major challenges for accurate network reconstruction and analysis.
• Future research must prioritize scalable, online learning algorithms and link prediction to restore missing network structures and support real-time transaction auditing.