A graph-matching model with theoretical guarantees. It proposes that checking the condition of a rule is equivalent to performing subgraph isomorphism tests. It trains a graph neural network (GNN) on the line graph and exploited the trained model with matching-based grading. The extracted rules server as a tool for the interpretability analysis.
