  We propose Embedding Propagation (EP), an unsupervised learning framework for
graph-structured data. EP learns vector representations of graphs by passing
two types of messages between neighboring nodes. Forward messages consist of
label representations such as representations of words and other attributes
associated with the nodes. Backward messages consist of gradients that result
from aggregating the label representations and applying a reconstruction loss.
Node representations are finally computed from the representation of their
labels. With significantly fewer parameters and hyperparameters an instance of
EP is competitive with and often outperforms state of the art unsupervised and
semi-supervised learning methods on a range of benchmark data sets.
