FraudLens: Graph Structural Learning for Bitcoin Illicit Activity Identification

Illicit activity in cryptocurrency has increased dramatically over the years. Bitcoin mechanics allow for users to mask their identity through obfuscation techniques. Much research has been published in the domain of identifying illicit activity in cryptocurrency, and in particular the emergence of Graph Neural Networks (GNNs) has shown great promise in this area. In this paper, we propose two graph preprocessing methods to improve performance and robustness of our node classification GNN models in identifying illicit transactions in the Bitcoin network. Our methods focus on graph restructuring through measuring the connectivity of nodes in a graph, and the similarity of the underlying features each node possesses. We demonstrate the graph restructuring methodologies on five GNN architectures and empirically show an improvement of evaluation metrics when compared against the unprocessed graph dataset. We compare our proposed methods against other imbalanced node classification techniques on a common graph dataset. This methodology has great opportunity in the transaction monitoring landscape for exchanges and financial institutions attempting to capture potential illicit activity taking place on their networks including money laundering.