GNN / HGNN Reasoning

Documentation status: guide — see Maturity and evidence.

GNN and HGNN modules learn from the structure of the conceptual graph or hypergraph.

They can support similarity detection, anomaly detection, risk prediction, graph propagation, neighborhood embeddings, clustering, workflow recommendation, and structural-pattern recognition.

Why hypergraph neural methods matter

Enterprise knowledge is frequently polyadic:

Invoice
  supplier
  amount
  project
  cost center
  validation rule
  approver
  context

This structure is not naturally reduced to independent binary edges. Hypergraph-oriented neural methods can better preserve the underlying multi-participant structure.