Reviews: PRUNE: Preserving Proximity and Global Ranking for Network Embedding

The paper presents a NN model for learning graph embeddings that preserves the local graph structure and a global node ranking similar to PageRank. The model is based on a Siamese network, which takes as inputs two node embeddings and compute a new (output) representation for each node using the Siamese architecture. Learning is unsupervised in the sense that it makes use only of the graph structure. Some links with a community detection criterion are also discussed. The model is evaluated on a series of tasks: node ranking, classification and regression, link prediction, and compared to other families of unsupervised embedding learning methods.