The Graphical Nadaraya-Watson Estimator on Latent Position Models
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Abstract (arXiv)
Given a graph with a subset of labeled nodes, we are interested in the quality of the averaging estimator which for an unlabeled node predicts the average of the observations of its labeled neighbors. We rigorously study concentration properties, variance bounds and risk bounds in this context. While the estimator itself is very simple we believe that our results will contribute towards the theoretical understanding of learning on graphs through more sophisticated methods such as Graph Neural Networks.
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References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2017Community Detection and Stochastic Block Modelscited by 317
- 2013Consistency of spectral clustering in stochastic block modelscited by 128
- 2015Sparse random graphs: regularization and concentration of the Laplaciancited by 3
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