Graph Neural Networks

Table of Contents Introduction Background 2.1. [Fraud in Financial Ledger Systems] 2.2. [Distributed Ledger Technologies (DLTs)] 2.3. [Traditional Fraud Detection Approaches] Representing Ledger Data as Graphs 3.1. [Node Types and Attributes] 3.2. [Edge Types and Temporal Information] 3.3. [Feature Engineering Example with NetworkX] Fundamentals of Graph Neural Networks 4.1. [Message‑Passing Framework] 4.2. [Popular GNN Architectures] 4.3. [Loss Functions for Anomaly Detection] Designing GNNs for Fraud Detection 5.1. [Supervised vs. Semi‑Supervised Learning] 5.2. [Handling Imbalanced Data] 5.3. [Temporal/Dynamic Graphs] 5.4. [Sample PyTorch Geometric Model] Case Study: Money‑Laundering Detection on a Permissioned Blockchain 6.1. [Dataset Overview] 6.2. [Graph Construction Pipeline] 6.3. [Training and Evaluation] 6.4. [Results & Interpretation] Practical Considerations for Production 7.1. [Scalability & Distributed Training] 7.2. [Privacy, Compliance, and Federated Learning] 7.3. [Model Explainability] Deployment Strategies 8.1. [Real‑Time Inference Architecture] 8.2. [Integration with AML/Compliance Suites] 8.3. [Monitoring & Model Drift] Future Directions Conclusion Resources Introduction Financial institutions are increasingly moving their transaction records onto distributed ledger technologies (DLTs)—public blockchains, permissioned ledgers, or directed‑acyclic‑graph (DAG) systems. While DLTs provide immutability, transparency, and auditability, they also introduce new attack surfaces. Fraudsters exploit the pseudonymous nature of many ledgers, creating complex, multi‑hop transaction patterns that evade classic rule‑based anti‑money‑laundering (AML) systems. ...

Demystifying Rumors on Social Media: How Pre-trained Propagation Tree Transformers Beat Over-Smoothing Rumors spread like wildfire on social media, often causing real-world chaos before the truth catches up. The research paper “Avoiding Over-smoothing in Social Media Rumor Detection with Pre-trained Propagation Tree Transformer” introduces a game-changing approach called P2T3 (Pre-trained Propagation Tree Transformer) that tackles a major flaw in traditional AI rumor detection methods.[4] This blog post breaks it down for a general technical audience, using simple analogies, real-world examples, and deep dives into why this matters. ...