Reviews: Learning Graph Representations with Embedding Propagation

The authors introduce embedding propagation (EP), a new message-passing method for learning representations of attributed vertices in graphs. EP computes vector representations of nodes from the'labels' (sparse features) associated with nodes and their neighborhood. The learning of these representations is facilitated by two different types of messages sent along edges: a'forward' message that sends the current representation of the node, and a'backward' message that passes back the gradients of some differentiable reconstruction loss. The authors report results that are competitive with or outperform baseline representation learning methods such as deepwalk and node2vec. Quality: The quality of the paper is high.