Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features

TL;DR

This paper introduces a convergent boosted smoothing framework for graph data with tabular node features, effectively combining boosting and graph propagation to improve predictive performance.

Contribution

It presents a novel, theoretically grounded method that integrates boosting with graph propagation, providing convergence guarantees and superior performance on non-iid graph datasets.

Findings

Achieves comparable or better accuracy than graph neural networks and hybrid models.

Offers a simple, efficient implementation with strong theoretical guarantees.

Demonstrates effectiveness across various non-iid graph datasets.

Abstract

For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets. However for graph data where the iid assumption is violated due to structured relations between samples, it remains unclear how to best incorporate this structure within existing boosting pipelines. To this end, we propose a generalized framework for iterating boosting with graph propagation steps that share node/sample information across edges connecting related samples. Unlike previous efforts to integrate graph-based models with boosting, our approach is anchored in a principled meta loss function such that provable convergence can be guaranteed under relatively mild assumptions. Across a variety of non-iid graph datasets with tabular node features, our method achieves comparable or superior performance than both tabular and graph neural network models, as well as existing hybrid strategies that combine the two. Beyond producing better predictive performance than recently proposed graph models, our proposed techniques are easy to implement, computationally more efficient, and enjoy stronger theoretical guarantees (which make our results more reproducible).