Visualizing Dynamic Bitcoin Transaction Patterns

Abstract

This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.

Improving data exploration in graphs with fuzzy logic and large-scale visualisation

“…More recently, Pienta et al [7] organised most of those works according to their opportunities for understanding Users can walk in, and collaboratively explore data presented in different ways (charts, maps, animations, graphs...). The image shows Bitcoin transaction being displayed [8].…”

“…network Finally, we will explore the potential of fuzzy queries and large-scale visualization for Data Exploration in the dataset of Bitcoin's transactions, which is one of our current lines of research [8].…”

“…In a previous work [8], it was proposed a visualisation of transactions using a force-directed graph layout with gravity provided by the SigmaJS library and its ForceAtlas2 continuous layout algorithm [41]. In it, a transaction is visualised as a single vertex, associated by edges to its component orange input and blue output vertexes.…”

“…However, looking at the visualisation, it is immediately evident that there are some transactions that do not look human-like, as they have plenty of links and interconnections. For more details on some of these patterns, we encourage the reader to check [8].…”

“…Among the three different case-studies presented, one focused on the Bitcoin network is currently allowing us to unveil unexpected transaction patterns and the observation of several evolving attacks [8]. Immersive large-scale visualisations and fuzzy queries allow rapid identification of interesting nodes, which sparked discussion and further investigation amongst researchers.…”