# Predictive Graph Digital Twin **Entity class:** Analytic model ## Definition A **predictive graph digital twin** is a continuously updated probabilistic representation of an entity embedded in a temporal network of people, devices, accounts, vehicles, locations, organizations, transactions, communications identifiers, and events. New observations update uncertain identities, relationships, latent states, and conditional forecasts. It is a twin of an investigative hypothesis, not a duplicate of a human being. It should preserve provenance, competing explanations, uncertainty, and legal purpose rather than collapsing association into a single threat score. ## Relationships - **structure:** [[wiki/Temporal Heterogeneous Graph|Temporal Heterogeneous Graph]]. - **updates through:** [[wiki/Data Assimilation|Data Assimilation]] and [[wiki/State Estimation|State Estimation]]. - **relationship layer:** [[wiki/Identity and Relationship Analysis|Identity and Relationship Analysis]]. - **computational acceleration:** [[wiki/Reduced-Order Modeling|Reduced-Order Modeling]]. - **ethical boundary:** [[wiki/Association Is Not Guilt|Association Is Not Guilt]] and provenance-aware uncertainty. ## Sources / Provenance - [[research/Oden Institute - Theater-to-Person Predictive Scale|Oden Institute: From Theater-Scale Fields to Person-Scale Receptors]] - [ODNI — Data Mining Report CY2021–2023](https://www.odni.gov/files/documents/CLPO/CY2021-2023_Data_Mining_Report_FINAL.pdf)