Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
30 use case guides showing how to apply graph neural networks to real problems. From fraud detection to drug discovery to recommendation systems.
HeteroConv on banking transaction graphs for real-time fraud ring detection.
Two-tower GraphSAGE for e-commerce recommendation engines.
GATConv on SaaS user-activity graphs for retention modeling.
Heterogeneous GNN on borrower-loan-institution graphs for default prediction.
Cycle-aware message passing on transaction graphs for AML detection.
Temporal graph networks for retail cross-store demand prediction.
NNConv for molecular property prediction on atom-bond graphs.
Link prediction on record similarity graphs for deduplication.
HeteroConv on B2B CRM graphs for conversion prediction.
Regression on purchase and interaction graphs for lifetime value.
GNN on user-content interaction graphs for media personalization.
Social and behavioral graphs for gaming retention prediction.
RGCNConv on claims networks for fraudulent claim detection.
Demand graph optimization for competitive pricing strategies.
GNN on EHR graphs for hospital readmission prediction.
GNN on BOM and supplier graphs for supply chain risk assessment.
GNN on user-ad interaction graphs for CTR prediction.
GNN on traffic flow graphs for cybersecurity threat detection.
Attention layers for influence propagation modeling.
GNN on supply-demand graphs for stock allocation.
Next-product prediction on purchase graphs.
GNN on telecom usage and network graphs for retention.
GNN on user-query-document graphs for personalized ranking.
GNN on manufacturing sensor graphs for anomaly detection.
GNN on location and transaction graphs for property pricing.
GNN on smart meter and weather graphs for grid load prediction.
GNN on enrollment graphs for academic outcome forecasting.
GNN on sponsor-site-condition graphs for trial outcome prediction.
GNN on agent-skill-history graphs for support routing.
GNN on customer-program graphs for rewards optimization.
KumoRFM handles graph construction, training, and serving for any relational prediction task. One line of PQL.