用量子启发图神经网络+集成模型提升区块链反洗钱欺诈检测
Blockchain Network Analysis using Quantum Inspired Graph Neural Networks & Ensemble Models
- 引入CP分解层增强图神经网络处理复杂结构的能力
- 在检测欺诈交易上取得74.8%的F2分数，优于传统方法
- 适合关注金融安全与新型机器学习融合的研究者
在金融科技快速发展的背景下，识别区块链网络中的非法交易仍是关键挑战，亟需稳健且创新的解决方案。本文提出一种新方法，将量子启发图神经网络（QI-GNN）与可选集成模型（QBoost或随机森林分类器）结合，专门用于反洗钱（AML）场景下的区块链网络分析。该系统创新性地在图神经网络框架中引入了规范多项式（CP）分解层，显著提升了对复杂数据结构的处理与分析效率。技术方案经过与经典机器学习方法的严格对比测试，在检测欺诈交易任务上实现了74.8%的F2分数。结果表明，量子启发技术结合CP层结构改进，不仅能匹配甚至超越传统方法在复杂网络分析中的表现。研究呼吁在金融领域更广泛采用并深入探索量子启发算法，以有效应对欺诈风险。
原文摘要 · Abstract (English)
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.
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