graph-classification

This paper presents the first model extraction attack on graph classification under strict black-box constraints, exploiting subgraph explanations to estimate decision boundaries. The findings reveal that mandated explainability interfaces create exploitable security vulnerabilities in Graph Neural Network services.

This paper introduces PathBoost, a gradient tree boosting method for graph-level prediction that uses path-based features to compete with graph neural networks while offering better interpretability.