Graph Analytics for Blockchain Fraud Detection: A Comprehensive Review and the GraphGuard Framework

The decentralized nature of blockchain ecosystems, while fostering transparency and trustless transactions, has simultaneously facilitated the rise of complex fraudulent activities. From multi-hop phishing and Ponzi schemes to sophisticated money laundering operations, the scale of illicit financial flows in cryptocurrency networks has become a significant challenge for regulatory compliance and network security. Traditional machine learning (ML) methodologies, which rely on handcrafted statistical features and local heuristics, often fail to capture the complex relational and structural dependencies inherent in large-scale transaction graphs. This paper provides a comprehensive review of blockchain fraud detection through the lens of graph analytics, with a specific focus on the evolution toward Graph Neural Networks (GNNs). We identify a critical research gap: the inability of existing models to effectively handle "camouflage" behavior where fraudsters intentionally interact with benign entities to obscure their structural identity. To address this, we propose GraphGuard, a modular framework that integrates spatial-temporal graph modeling with heterophily-aware aggregation strategies. Our methodology evaluates the transition from topological modeling to dynamic sequence analysis, highlighting how GNNs address structural neglect and temporal evolution. Analysis of real-world datasets reveals that spatial-temporal models achieve detection accuracy exceeding 98%, significantly outperforming traditional ML. The contributions of this work include a systematic categorization of contemporary research, the design of a novel detection architecture, and a critical analysis of adversarial evasion and scalability. This synthesis serves as a roadmap for developing high-fidelity security solutions in dynamic blockchain environments, providing both theoretical foundations and practical insights for researchers and practitioners working to secure the over $82 billion in cryptocurrencies currently at risk.