Chainlet Orbits: Topological Address Embedding for the Bitcoin
  Blockchain
Poupak Azad Baris Coskunuzer Murat Kantarcioglu Cuneyt Gurcan Akcora
The rise of cryptocurrencies like Bitcoin, which enable transactions with a
degree of pseudonymity, has led to a surge in various illicit activities,
including ransomware payments and transactions on darknet markets. These
illegal activities often utilize Bitcoin as the preferred payment method.
However, current tools for detecting illicit behavior either rely on a few
heuristics and laborious data collection processes or employ computationally
inefficient graph neural network (GNN) models that are challenging to
interpret.
  To overcome the computational and interpretability limitations of existing
techniques, we introduce an effective solution called Chainlet Orbits. This
approach embeds Bitcoin addresses by leveraging their topological
characteristics in transactions. By employing our innovative address embedding,
we investigate e-crime in Bitcoin networks by focusing on distinctive
substructures that arise from illicit behavior.
  The results of our node classification experiments demonstrate superior
performance compared to state-of-the-art methods, including both topological
and GNN-based approaches. Moreover, our approach enables the use of
interpretable and explainable machine learning models in as little as 15
minutes for most days on the Bitcoin transaction network.