AssureGraph: Graph-Based Evaluation of Human vs LLM-Generated Assurance Cases This repository contains the official implementation of the paper: Evaluating Assurance Cases as Text-Attributed Graphs for Predicate Structure and Provenance AnalysisEASE 2026 AI AssureGraph introduces a graph evaluation framework for analysing the semantic and structural patterns of assurance cases. These are structured argument documents used in safety, security, and regulatory compliance. We model assurance cases as Text-Attributed Argument Graphs (TAGs) and evaluate them using Graph Neural Networks (GNNs) for: Link Prediction — identify connections between argument elements Graph Classification — distinguishing between human-authored and LLM-generated cases Explainability — analysing node/edge importance using GNNExplainer This repository provides: A cleaned and curated public dataset of assurance cases Scripts for graph construction, training, and evaluation Reproducible experiments for link prediction, provenance classification, and GNN explainability Utilities to visualise structural differences between human and LLM-generated cases