  Recently, graph neural networks (GNNs) have proved to be suitable in tasks on
unstructured data. Particularly in tasks as community detection, node
classification, and link prediction. However, most GNN models still operate
with static relationships. We propose the Graph Learning Network (GLN), a
simple yet effective process to learn node embeddings and structure prediction
functions. Our model uses graph convolutions to propose expected node features,
and predict the best structure based on them. We repeat these steps recursively
to enhance the prediction and the embeddings.
