Abstract: A computer-implemented method can include providing a data set arranged into multiple nodes and assigning a random variable and a hyper-parameter to at least one pair of the multiple nodes. The hyper-parameter can define a current probability distribution of the random variable. The method can further include: causing the random variable to occupy a discrete state based on the current probability distribution; sampling a graph structure for the data set based on the discrete state; adjusting a weight of a prediction model based on the sampled graph structure; estimating a gradient of the hyper-parameter based on the sampled graph structure and the adjusted weight; adjusting the hyper-parameter based on the estimated gradient; resampling a graph structure for the data set based on the adjusted hyper-parameter; and assigning a final graph structure to the data set based on the resampled graph structure.