graph-machine-learning

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.

This paper introduces GCCM, a graph contrastive consistency model that improves generative graph prediction by mitigating shortcut solutions in consistency training through negative pairs and feature perturbation.