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

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