Graph Neural Networks in Production: Applications, Architectures, and Best Practices
GCN, GAT, GraphSAGE for fraud detection, recommendation, and molecular design
Graph Neural Networks in Production: Applications, Architectures, and Best Practices
GCN, GAT, GraphSAGE for fraud detection, recommendation, and molecular design
Learn practical applications of Graph Neural Networks including fraud detection in financial transactions, molecule property prediction, knowledge graph completion, and large-scale recommendation systems.
Graph Neural Networks (GNNs) excel at problems with relational structure. Core message passing paradigm: nodes aggregate information from neighbors, update their representations, repeat for k layers. GCN (Kipf 2017): uses normalized adjacency for message passing - simple but limited to transductive settings. GraphSAGE: samples fixed-size neighborhoods for inductive learning - scales to billion-node graphs (used in Pinterest PinSage). Graph Attention Networks (GAT): learn attention weights for different neighbors - better performance for heterogeneous graphs. Applications: 1) Fraud detection - model transaction network as graph (users, merchants, cards as nodes, transactions as edges), GNN propagates fraud signals through network, detecting ring fraud invisible to per-transaction models. 2) Drug discovery - molecular graphs (atoms as nodes, bonds as edges), predict ADMET properties (absorption, distribution, metabolism, excretion, toxicity) for drug candidates. 3) Recommendation - user-item interaction graph, GraphSAGE generates embeddings for new users/items without retraining. 4) Knowledge graphs - entity and relation embeddings for link prediction (TransE, RotatE, ComplEx). Libraries: PyTorch Geometric (PyG) for research, DGL for production large-scale graphs, GraphBolt for sampling at scale.