Bayesian Semi-supervised Learning with Graph Gaussian Processes

Yin Cheng Ng; Nicolo Colombo; Ricardo Silva

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arXiv · 1809.04379

Bayesian Semi-supervised Learning with Graph Gaussian Processes

Yin Cheng Ng·Nicolo Colombo·Ricardo Silva

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Abstract

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. Furthermore, the model does not require a validation data set for early stopping to control over-fitting. Our model can be viewed as an instance of empirical distribution regression weighted locally by network connectivity. We further motivate the intuitive construction of the model with a Bayesian linear model interpretation where the node features are filtered by an operator related to the graph Laplacian. The method can be easily implemented by adapting off-the-shelf scalable variational inference algorithms for Gaussian processes.

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Yin Cheng Ng, Nicolo Colombo, Ricardo Silva. 2018-09-12. Bayesian Semi-supervised Learning with Graph Gaussian Processes. https://arxiv.org/abs/1809.04379

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