Ng, YC; Colombo, N; Silva, R; (2018) Bayesian Semi-supervised Learning with Graph Gaussian Processes. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.) Neural Information Processing Systems 31. Neural Information Processing Systems Foundation, Inc.: Montreal, Canada.

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Abstract

We propose a data-efficient Gaussian process-based Bayesian approach to the semisupervised 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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