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. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.
@article{arxiv.1603.08861,
title = {Revisiting Semi-Supervised Learning with Graph Embeddings},
author = {Zhilin Yang and William W. Cohen and Ruslan Salakhutdinov},
journal= {arXiv preprint arXiv:1603.08861},
year = {2016}
}