PathBoost is a gradient boosting method for predicting properties of entire graphs (like molecules or social networks) by learning discriminative path-based features directly from graph structure. It extends previous work by handling binary classification, incorporating multiple node/edge attributes through prefix decomposition, and automatically selecting anchor nodes (starting points for paths) based on attribute diversity. The method matches or beats graph neural networks and graph kernel methods on several benchmarks, especially for graphs with many nodes.
Main takeaways:
- Uses gradient tree boosting to learn which paths through a graph are predictive of the target property
- Automatically picks anchor nodes based on how diverse their categorical attributes are, removing the need for manual specification
- Handles multiple node and edge attributes by decomposing them into prefix-based features
- Competitive with or better than graph neural networks on half of benchmark datasets, particularly strong on larger graphs
- Offers an interpretable alternative to black-box graph neural networks by explicitly identifying discriminative path patterns