Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features

• URL: http://arxiv.org/abs/2110.13413v1
• Date: Tue, 26 Oct 2021 04:53:12 GMT
• Title: Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features
• Authors: Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf
• Abstract summary: We propose a framework for iterating boosting with graph propagation steps. Our approach is anchored in a principled meta loss function. Across a variety of non-iid graph datasets, our method achieves comparable or superior performance.
• Score: 46.052312251801
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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