An adaptive graph-based method for structured learning and decision analysis

Many modern decision-support systems operate as networks of interacting units—for example, departments in a hospital, hubs in a transportation system, or teams inside a large organization. Each unit may use its own data representation and its own decision model. This paper studies how to make reliable network-wide decisions when local models are heterogeneous and uncertainty is present. We introduce the Parameter Learning Quantum Graph (PLQG), a graph-based framework in which each node has its own local parameter/state space and each directed edge carries a transport operator that specifies how a state from one node should be interpreted at another node. The framework supports time-varying networks, heterogeneous models, and interpretable measures of cross-unit consistency. We also distinguish event-driven (discrete) and continuous interactions. • Introduce a structured graph framework for analytical learning and decision modeling. • Define parameter transports that enable transfer, adaptation, and interpretability. • Unify graph learning with geometric reasoning and structured analytics. • Demonstrate performance gains in classification, adaptation, and decision tasks. • Show scalability and interpretability in complex and distributed learning settings.