Graph Neural Networks in Production: Applications, Architectures, and Best Practices

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.