GNN

At CERN, we apply graph neural networks (GNNs) to model complex systems of systems across the accelerator technical infrastructure and healthcare initiatives. Representing assets, signals, and processes as nodes and relationships: mechanical as edges, GNNs fuse topology with telemetry to detect anomalies, predict cascading failures, and optimize maintenance, improving uptime and safety for the accelerator technology sector (i.e., cryogenics). The same approach integrates longitudinal clinical data, encounters, diagnostics, therapies into patient-centric graphs that enable risk stratification, care-pathway optimization, and earlier interventions. Implementations leverage spatiotemporal and heterogeneous GNNs, digital twins for what-if analysis, and privacy-preserving (e.g., federated) training where required. Outcomes include reduced unplanned downtime, faster root-cause analysis, and more accurate forecasts with fewer false alarms, while in healthcare the focus is on improved triage and resource allocation. This graph-first strategy delivers actionable intelligence from complex interdependencies, aligning with operational reliability, regulatory constraints, and measurable return on investment.