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Cryptocurrency Network Analysis

Natkamon Tovanich, Célestin Coquidé, Rémy Cazabet

TL;DR

This paper surveys cryptocurrency network analysis by applying social network analysis to Bitcoin and Ethereum transaction data, contrasting value transfers with textual social content. It discusses multiple network representations (address-transaction bipartite graphs, various projection networks, and surrogate user networks via address clustering) and how they enable insights into structure, dynamics, wealth distribution, and illicit activity. It covers Ethereum's account-based model, token ecosystems (ERC-20, ERC-721), DeFi, NFT markets, and MEV, alongside methods like graph ML and GNNs for forensics and risk assessment. The work emphasizes data accessibility, tools for extracting blockchain data, and future directions including Smart Contracts, DeFi, and Metaverse integration to advance socio-technical understanding and practical risk management.

Abstract

Cryptocurrency network analysis consists of applying the tools and methods of social network analysis to transactional data issued from cryptocurrencies. The main difference with most online social networks is that users do not exchange textual content but instead value -- in systems designed mainly as cryptocurrency, such as Bitcoin -- or digital items and services in more permissive systems based on smart contracts such as Ethereum.

Cryptocurrency Network Analysis

TL;DR

This paper surveys cryptocurrency network analysis by applying social network analysis to Bitcoin and Ethereum transaction data, contrasting value transfers with textual social content. It discusses multiple network representations (address-transaction bipartite graphs, various projection networks, and surrogate user networks via address clustering) and how they enable insights into structure, dynamics, wealth distribution, and illicit activity. It covers Ethereum's account-based model, token ecosystems (ERC-20, ERC-721), DeFi, NFT markets, and MEV, alongside methods like graph ML and GNNs for forensics and risk assessment. The work emphasizes data accessibility, tools for extracting blockchain data, and future directions including Smart Contracts, DeFi, and Metaverse integration to advance socio-technical understanding and practical risk management.

Abstract

Cryptocurrency network analysis consists of applying the tools and methods of social network analysis to transactional data issued from cryptocurrencies. The main difference with most online social networks is that users do not exchange textual content but instead value -- in systems designed mainly as cryptocurrency, such as Bitcoin -- or digital items and services in more permissive systems based on smart contracts such as Ethereum.

Paper Structure

This paper contains 13 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Key Points
  3. History and Technical Premises
  4. Cryptocurrencies as Socio-Technical Systems
  5. Cryptocurrency Transaction Analysis on Bitcoin
  6. Bitcoin transaction networks
  7. Research questions and key findings
  8. Smart Contract Analysis on Ethereum
  9. Ethereum network models
  10. Research questions and key findings
  11. Data Accessibility for Public Blockchains
  12. Key Applications
  13. Future Directions

Figures (4)

  • Figure 1: Bitcoin transaction network representations.
  • Figure 2: Illustration of the address clustering approach based on the multi-input heuristic.
  • Figure 3: Visualization of the Bitcoin user network.
  • Figure 4: Ethereum network representations.