Tracing how money moves across tokens in cryptocurrency networks
Tracing Money Flows in the Multi-Token Cryptocurrency Network
Social and Information NetworksComputational Engineering, Finance, and ScienceComputers and SocietyEmerging TechnologiesInformation Retrieval
Summary
It is hard to follow how money moves inside cryptocurrency networks because funds can change accounts, transactions, and types of tokens. The authors created a special graph that tracks these complex movements by representing different states of accounts and assets, and showing how they connect through transfers and time. They tested their approach on a borrowing and repayment system called Aave, comparing ways to build and simplify the graph to find clear paths of asset flows. Their method helps reveal common sequences of token transformations before repayment, making complex multi-token flows easier to understand.
What this means in practice
- •For blockchain analysts: Trace and interpret multi-token asset flows in complex decentralized finance transactions for auditing and compliance.
- •For cryptocurrency security teams: Identify suspicious token transfer patterns across multiple tokens and transactions to enhance fraud detection.
Tested on one dataset.
Authors
Natkamon Tovanich, Célestin Coquidé
Abstract
Tracing money flows in cryptocurrency transaction networks is challenging because funds can move across accounts, transactions, and token layers. We propose a transaction-aware graph in which nodes represent account--asset states within transactions, while edges encode observed transfers, inferred asset transformations, and temporal inter-transaction sequences. Applied to Aave borrow--repay flows, we compare graph construction and edge-pruning strategies and extract shortest paths using a heuristic weighting. Our results reveal a trade-off between reachability and interpretability and uncover frequent token sequences underlying how borrowed assets are transformed before repayment. This work lays a foundation for general, interpretable multi-token flow tracing.