k -Nearest Neighbor Learning with Graph Neural Networks

k -nearest neighbor ( k NN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using k NN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k , the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel k NN learning method based on a graph neural network, named k NNGNN. Given training data, the method learns a task-specific k NN rule in an end-to-end fashion by means of a graph neural network that takes the k NN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a k NN search from the training data to create a k NN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.