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Graph Visualization for Blockchain Data

Marcell Dietl, Andre Gemünd, Daniel Oeltz, Felix M. Thiele, Christian Werner

TL;DR

This work tackles scalable visualization of blockchain transaction graphs with very large node counts. It introduces a hybrid layout pipeline that combines Kamada–Kawai for small components and a Fast Multipole–accelerated ForceAtlas2 for large components, followed by a principled reassembly of component layouts. The method balances layout quality and computational efficiency, enabling practical layouts for graphs with tens of thousands to hundreds of thousands of nodes and beyond, demonstrated on Bitcoin transaction graphs. The approach generalizes to other blockchain datasets and large graphs from diverse domains, with public source code available on GitHub.

Abstract

In this report, we introduce a novel approach to visualize extremely large graphs efficiently. Our method combines two force-directed algorithms, Kamada-Kawai and ForceAtlas2, to handle different graph components based on their node count. Additionally, we suggest utilizing the Fast Multipole method to enhance the speed of ForceAtlas2. Although initially designed for analyzing bitcoin transaction graphs, for which we present results here, this algorithm can also be applied to other crypto currency transaction graphs or graphs from diverse domains.

Graph Visualization for Blockchain Data

TL;DR

This work tackles scalable visualization of blockchain transaction graphs with very large node counts. It introduces a hybrid layout pipeline that combines Kamada–Kawai for small components and a Fast Multipole–accelerated ForceAtlas2 for large components, followed by a principled reassembly of component layouts. The method balances layout quality and computational efficiency, enabling practical layouts for graphs with tens of thousands to hundreds of thousands of nodes and beyond, demonstrated on Bitcoin transaction graphs. The approach generalizes to other blockchain datasets and large graphs from diverse domains, with public source code available on GitHub.

Abstract

In this report, we introduce a novel approach to visualize extremely large graphs efficiently. Our method combines two force-directed algorithms, Kamada-Kawai and ForceAtlas2, to handle different graph components based on their node count. Additionally, we suggest utilizing the Fast Multipole method to enhance the speed of ForceAtlas2. Although initially designed for analyzing bitcoin transaction graphs, for which we present results here, this algorithm can also be applied to other crypto currency transaction graphs or graphs from diverse domains.
Paper Structure (8 sections, 3 equations, 5 figures)

This paper contains 8 sections, 3 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Bitcoin Transaction Graphs
  3. Force-Directed Algorithms
  4. Algorithm from Kamada and Kawai
  5. ForceAtlas2 with Fast Multipole
  6. Fast Multipole Algorithm
  7. Final Algorithm
  8. Summary

Figures (5)

  • Figure 1: Number of bitcoin transactions and addresses on a monthly basis.
  • Figure 2: Schematic illustration of a blockchain.
  • Figure 3: Illustration of the overall algorithm for blockchain data visualization.
  • Figure 4: A visualization of the transaction graph on April 19, 2011. Transaction nodes are red, address nodes are blue. Contains around $1.5\cdot10^4$ nodes.
  • Figure 5: A visualization of the transaction graph on December 12, 2013. Transaction nodes are red, address nodes are blue. Contains around $2\cdot10^5$ nodes.