Path-Based Gradient Boosting for Graph-Level Prediction

TL;DR

PathBoost is a gradient boosting method for graph-level prediction that learns path-based features directly from graph structures, showing competitive performance with neural networks and kernels.

Contribution

It introduces a novel path-based gradient boosting approach with extensions for classification, attribute integration, and automatic anchor node selection.

Findings

PathBoost outperforms some graph neural networks and kernels on benchmark datasets.

It performs better on graphs with larger average node counts.

PathBoost is competitive with complex black-box models.

Abstract

We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. Building on a previous work, which was tailored to a specific chemistry application, PathBoost introduces three key extensions: (i) adaptation to binary classification through gradient boosting with a logistic loss, (ii) incorporation of multiple node and edge attributes into the path feature space via a prefix-based decomposition, and (iii) automatic anchor node selection based on categorical attribute diversity, eliminating the need for the user to specify the starting point of the considered path features. We compared PathBoost to graph neural networks and graph kernel approaches on several benchmark datasets, obtaining better results in half of them, and comparable results in the rest. PathBoost shows better performances on graphs with larger average node counts. Overall, the results demonstrate that path-based boosting methods can be competitive with more complex black-box approaches.