Miguel Molina-Solana

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.

@article{McGinn2016,
  title = {Visualizing dynamic {B}itcoin transaction patterns},
  author = {McGinn, Dan and Birch, David and Akroyd, David and Molina-Solana, Miguel and Guo, Yi-ke and Knottenbelt, William},
  journal = {Big Data},
  year = {2016},
  number = {2},
  pages = {109--119},
  volume = {4},
  comment = {https://www.doc.ic.ac.uk/~mmolinas/publications/mcginn-bd16.pdf},
  doi = {10.1089/big.2015.0056},
  timestamp = {08}
}