# Graph Attention Network **Entity class:** AI And Graph-Learning Layer concept **Graph Attention Network** is a representation in which entities or states are nodes and typed relationships or transitions are edges. The operational value of Graph Attention Network lies in preserving time, provenance, confidence, and alternative explanations. ## Counterterrorism predictive-graph role The page records capability and analytic function. Any claim of a named operational deployment remains subject to the [[wiki/Counterterrorism Predictive Graph Evidence Ladder|evidence ladder]]. The analytic state should distinguish ground truth, observed evidence, inferred state, and predicted state. ## Relationships - **domain router:** [[wiki/Graph Machine Learning|Graph Machine Learning]]. - **ontology neighbors:** [[wiki/Graph Convolutional Network|Graph Convolutional Network]] and [[wiki/Graph Transformer|Graph Transformer]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. ## Sources - [IARPA — AGILE technical overview (accessed September 23, 2026)](https://www.iarpa.gov/images/research-programs/AGILE/AGILE_ModSim_-_Final.pdf) - [IARPA — Making Data Analysis More AGILE (accessed September 23, 2026)](https://www.iarpa.gov/newsroom/article/making-data-analysis-more-agile) **As of:** 2026-09-23