Blockchain Network Analysis using Quantum Inspired Graph Neural Networks & Ensemble Models
This addresses fraud detection in blockchain for financial security, but it appears incremental as it builds on existing quantum-inspired and graph neural network methods.
The paper tackled detecting illicit transactions in blockchain networks for anti-money laundering by proposing a Quantum Inspired Graph Neural Network with a Canonical Polyadic decomposition layer and ensemble models, achieving an F2 score of 74.8% in fraud detection.
In the rapidly evolving domain of financial technology, the detection of illicit transactions within blockchain networks remains a critical challenge, necessitating robust and innovative solutions. This work proposes a novel approach by combining Quantum Inspired Graph Neural Networks (QI-GNN) with flexibility of choice of an Ensemble Model using QBoost or a classic model such as Random Forrest Classifier. This system is tailored specifically for blockchain network analysis in anti-money laundering (AML) efforts. Our methodology to design this system incorporates a novel component, a Canonical Polyadic (CP) decomposition layer within the graph neural network framework, enhancing its capability to process and analyze complex data structures efficiently. Our technical approach has undergone rigorous evaluation against classical machine learning implementations, achieving an F2 score of 74.8% in detecting fraudulent transactions. These results highlight the potential of quantum-inspired techniques, supplemented by the structural advancements of the CP layer, to not only match but potentially exceed traditional methods in complex network analysis for financial security. The findings advocate for a broader adoption and further exploration of quantum-inspired algorithms within the financial sector to effectively combat fraud.