Chainlet Orbits: Topological Address Embedding for Blockchain

Abstract

The rise of cryptocurrencies like Bitcoin has not only increased trade volumes but also broadened the use of graph machine learning techniques, such as address embeddings, to analyze transactions and decipher user patterns. Traditional analysis methods rely on simple heuristics and extensive data gathering, while more advanced Graph Neural Networks encounter challenges such as scalability, poor interpretability, and label scarcity in massive blockchain transaction networks. To overcome existing techniques’ computational and interpretability limitations, we introduce a topological approach, Chainlet Orbits, which embeds blockchain addresses by leveraging their topological characteristics in temporal transactions. We employ our innovative address embeddings to investigate financial behavior and e-crime in the Bitcoin and Ethereum networks, focusing on distinctive substructures that arise from user behavior. Our model demonstrates exceptional performance in node classification experiments compared to GNN-based approaches. Furthermore, our approach embeds all daily nodes of the largest blockchain transaction network, Bitcoin, and creates explainable machine learning models in less than 17 minutes which takes days for GNN-based approaches.