gradient boosting

Path-Based Gradient Boosting for Graph-Level Prediction

We propose PathBoost, a gradient tree boosting method for graph-level classification and regression, which learns discriminative path-based features directly from the input graph structure. This method introduces adaptations for binary classification, incorporates multiple node and edge attributes, and automatically selects anchor nodes, outperforming or matching graph neural networks and graph kernel approaches on several benchmark datasets.