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

arXiv: https://arxiv.org/abs/2110.13413 · HTML: https://mlsanity.com/paper/2110.13413/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 29 of 29)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [On the Bottleneck of Graph Neural Networks and its Practical Implications](https://mlsanity.com/paper/2006.05205/) (2020)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Generalization and Representational Limits of Graph Neural Networks](https://mlsanity.com/paper/2002.06157/) (2020)
- [Graph Neural Networks with Heterophily](https://mlsanity.com/paper/2009.13566/) (2020)
- [Combining Label Propagation and Simple Models Out-performs Graph Neural Networks](https://mlsanity.com/paper/2010.13993/) (2020)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [Interpreting and Unifying Graph Neural Networks with An Optimization Framework](https://mlsanity.com/paper/2101.11859/) (2021)
- [A Unified View on Graph Neural Networks as Graph Signal Denoising](https://mlsanity.com/paper/2010.01777/) (2020)
- [Attention-based Graph Neural Network for Semi-supervised Learning](https://mlsanity.com/paper/1803.03735/) (2018)
- [Graph Neural Networks Inspired by Classical Iterative Algorithms](https://mlsanity.com/paper/2103.06064/) (2021)
- [AdaNet: Adaptive Structural Learning of Artificial Neural Networks](https://mlsanity.com/paper/1607.01097/) (2016)
- [Understanding and Resolving Performance Degradation in Graph Convolutional Networks](https://mlsanity.com/paper/2006.07107/) (2020)
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://mlsanity.com/paper/2101.02118/) (2021)
- [Boost then Convolve: Gradient Boosting Meets Graph Neural Networks](https://mlsanity.com/paper/2101.08543/) (2021)
- [Bag of Tricks for Node Classification with Graph Neural Networks](https://mlsanity.com/paper/2103.13355/) (2021)
- [AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models](https://mlsanity.com/paper/1908.05081/) (2019)
- [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://mlsanity.com/paper/2006.14284/) (2020)
- [Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective](https://mlsanity.com/paper/2009.11469/) (2020)
- [Gradient boosting for extreme quantile regression](https://mlsanity.com/paper/2103.00808/) (2021)
- [Randomized Gradient Boosting Machine](https://mlsanity.com/paper/1810.10158/) (2018)
- [Kernel-based Inference of Functions over Graphs](https://mlsanity.com/paper/1711.10353/) (2017)
- [Should Graph Neural Networks Use Features, Edges, Or Both?](https://mlsanity.com/paper/2103.06857/) (2021)
- [Understanding the Message Passing in Graph Neural Networks via Power Iteration Clustering](https://mlsanity.com/paper/2006.00144/) (2020)

## Built on by (most cited first, 9 of 9)

- [GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2306.12251/) (2023)
- [Turning Tabular Foundation Models into Graph Foundation Models](https://mlsanity.com/paper/2508.20906/) (2025)
- [Automated Data Curation for Robust Language Model Fine-Tuning](https://mlsanity.com/paper/2403.12776/) (2024)
- [Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2206.11081/) (2022)
- [Efficient Link Prediction via GNN Layers Induced by Negative Sampling](https://mlsanity.com/paper/2310.09516/) (2023)
- [Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks](https://mlsanity.com/paper/2212.04537/) (2022)
- [MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy](https://mlsanity.com/paper/2310.12457/) (2023)
- [BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization](https://mlsanity.com/paper/2403.04763/) (2024)
- [High-Order Optimization of Gradient Boosted Decision Trees](https://mlsanity.com/paper/2211.11367/) (2022)

## Abstract (arXiv)

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).