  We consider a family of problems that are concerned about making predictions
for the majority of unlabeled, graph-structured data samples based on a small
proportion of labeled samples. Relational information among the data samples,
often encoded in the graph/network structure, is shown to be helpful for these
semi-supervised learning tasks. However, conventional graph-based
regularization methods and recent graph neural networks do not fully leverage
the interrelations between the features, the graph, and the labels. In this
work, we propose a flexible generative framework for graph-based
semi-supervised learning, which approaches the joint distribution of the node
features, labels, and the graph structure. Borrowing insights from random graph
models in network science literature, this joint distribution can be
instantiated using various distribution families. For the inference of missing
labels, we exploit recent advances of scalable variational inference techniques
to approximate the Bayesian posterior. We conduct thorough experiments on
benchmark datasets for graph-based semi-supervised learning. Results show that
the proposed methods outperform the state-of-the-art models in most settings.
