In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{McGinn:2016:10.1089/big.2015.0056,
author = {McGinn, D and Birch, DA and Akroyd, D and Molina-Solana, M and Guo, Y and Knottenbelt, W},
doi = {10.1089/big.2015.0056},
journal = {Big Data},
pages = {109--119},
title = {Visualizing Dynamic Bitcoin Transaction Patterns},
url = {http://dx.doi.org/10.1089/big.2015.0056},
volume = {4},
year = {2016}
}
TY - JOUR
AB - 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.
AU - McGinn,D
AU - Birch,DA
AU - Akroyd,D
AU - Molina-Solana,M
AU - Guo,Y
AU - Knottenbelt,W
DO - 10.1089/big.2015.0056
EP - 119
PY - 2016///
SN - 2167-647X
SP - 109
TI - Visualizing Dynamic Bitcoin Transaction Patterns
T2 - Big Data
UR - http://dx.doi.org/10.1089/big.2015.0056
UR - http://hdl.handle.net/10044/1/32752
VL - 4
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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