Visualizing dynamic Bitcoin transaction patterns

Published in Big Data, vol 4(2), pp 109-119, 2016

Recommended citation: McGinn, D., Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y.-k. & Knottenbelt, W. (2016), "Visualizing dynamic Bitcoin transaction patterns", Big Data Vol. 4(2), pp. 109-119. http://doi.org/10.1089/big.2015.0056

A paper on Visualisation and Bitcoin.

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Recommended citation: McGinn, D., Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y.-k. & Knottenbelt, W. (2016), "Visualizing dynamic Bitcoin transaction patterns", Big Data Vol. 4(2), pp. 109-119.

Abstract: This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behaviour. 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 behavioural patterns of interest to many parties such as financial regulators, protocol designers and security analysts. However retaining visual fidelity to the underlying data in order 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 amongst domain experts and the general public alike. The high fidelity visualizations demonstrated in this paper 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.

BibTeX: @article{McGinn2016, author = {Dan McGinn and David Birch and David Akroyd and Miguel Molina-Solana and Yi-ke Guo and William Knottenbelt}, title = {Visualizing dynamic Bitcoin transaction patterns}, journal = {Big Data}, year = {2016}, volume = {4}, number = {2}, pages = {109--119}, doi = {http://doi.org/10.1089/big.2015.0056} }