graph-classification

This paper proposes a K-Hop Gaussian (KHG) diffusion kernel as a preprocessing module for graph neural networks, balancing local and global information propagation to mitigate over-smoothing and information bottlenecks. Experiments show significant improvements over traditional message-passing GNNs and existing diffusion kernels, especially on noisy or structurally complex graphs.

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.