Become an Inspector for a Day and Detect Fraudsters With Graph ML on Memgraph!

The blog post explores the application of Graph Machine Learning (Graph ML) in fraud detection, utilizing Memgraph, an in-memory database optimized for graph data. It highlights Graph ML as a burgeoning field, where data is represented as nodes and edges in a graph, and focuses on Node Classification, a process where a neural network predicts node labels based on their neighbors. The post details how Memgraph facilitates this process with query modules like Node2Vec and provides examples using real-world datasets such as the Yelp-Fraud dataset and a custom heterogeneous insurance fraud dataset. It explains the importance of initializing feature vectors for nodes and the use of algorithms such as Graph Attention Network with Jumping Knowledge (GAT+JK) to handle imbalanced datasets typical in fraud detection. Practical instructions for data loading, model training, and evaluation using Memgraph and visualization tools like Jupyter notebooks are provided, illustrating the potential of Graph ML in identifying fraudulent activities across sectors.