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Topological Analysis of Mixer Activities in the Bitcoin Network

Francesco Zola, Jon Ander Medina, Andrea Venturi, Raul Orduna

TL;DR

This paper addresses the challenge of understanding mixer operations in Bitcoin by proposing a graph-based framework that analyzes address-transaction topologies to reveal mixer modus operandi. It constructs mixer-focused address-transaction graphs with a maximum path length of $2n$ (where $n=2$) and employs Louvain community detection followed by clustering with OPTICS and HDBSCAN to extract recurrent topologies. Applying the method to Blender.io, it identifies consistent structural patterns and highlights the prominent role of Exchanges, raising AML/KYC concerns. The work represents an initial step toward actionable intelligence on mixer operations and lays the groundwork for incorporating intra-graph dynamics and economic signals in future research.

Abstract

Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.

Topological Analysis of Mixer Activities in the Bitcoin Network

TL;DR

This paper addresses the challenge of understanding mixer operations in Bitcoin by proposing a graph-based framework that analyzes address-transaction topologies to reveal mixer modus operandi. It constructs mixer-focused address-transaction graphs with a maximum path length of (where ) and employs Louvain community detection followed by clustering with OPTICS and HDBSCAN to extract recurrent topologies. Applying the method to Blender.io, it identifies consistent structural patterns and highlights the prominent role of Exchanges, raising AML/KYC concerns. The work represents an initial step toward actionable intelligence on mixer operations and lays the groundwork for incorporating intra-graph dynamics and economic signals in future research.

Abstract

Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.

Paper Structure

This paper contains 8 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Graph-Based Approach
  4. Address-Transaction Graph
  5. Topological Analysis
  6. Data Overview & Configuration
  7. Preliminary Results
  8. Conclusion and Future Work

Figures (4)

  • Figure 1: Example of modified 1-step address-transaction graph.
  • Figure 2: Number of mixer addresses in each address-transaction graph created.
  • Figure 3: Preliminary results obtained using HDBSCAN and OPTICS clustering algorithms.
  • Figure 4: Predominant topologies within cluster IDs 4. The width of the edges indicates the amount sent/received.