Road to NODES AI - Design Agent-Driven GraphRAG Systems with GNNs for Healthcare

​This session is designed to demonstrate how GraphRAG architectures can be applied to healthcare and other regulated domains where data is heterogeneous, distributed, and subject to stringent governance requirements. You will explore how knowledge graphs, graph neural networks (GNNs), and LLM-based agents collaborate to overcome the limitations of traditional text-centric RAG approaches, particularly in terms of temporal reasoning, explainability, and compliance.

​The workshop introduces an agent-driven GraphRAG architecture built on Neo4j that unifies medical ontologies, clinical documents, and research publications in a materialized knowledge graph, while keeping sensitive electronic health record (EHR) data virtually integrated rather than centralized. Through architectural walkthroughs and examples, you will see how agents plan and orchestrate graph queries, apply GNN-based relevance and similarity scoring, and enforce controlled access to regulated data sources.

​By grounding retrieval and reasoning in graph structure, this approach enables multi-hop, temporally aware reasoning with traceable and explainable outputs. You will leave with a clear understanding of when and how to combine knowledge graphs, GNNs, and agents to design GraphRAG systems suitable for high-trust environments such as healthcare.

​You will learn:

• ​How to design an agent-driven GraphRAG architecture for regulated medical domains

• ​How knowledge graphs unify ontologies, documents, and reports while preserving data privacy

• ​How GNNs support relevance, similarity scoring, and multi-hop reasoning

• ​How to separate structural knowledge from patient-level data for governance and compliance

• ​How graph-powered RAG enables explainable, temporally grounded answers