Revisiting Semi-Supervised Learning with Graph Embeddings

Understand

We present a semi-supervised learning framework based on graph embeddings.

• Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph.
• We develop both transductive and inductive variants of our method.
• In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training.