Learning a graph from nodal features is a central problem in network science and statistics, with a history spanning more than 50 years. In recent years, numerous graph-learning algorithms have emerged from the field of graph signal processing (GSP). This talk has three main objectives: (i) to explain different GSP-based graph-learning methods and compare them with classical statistical approaches, (ii) to review recent GSP-based graph-learning results, and (iii) to briefly discuss current trends, challenges, and future directions. While the focus will be on the so-called network association problem (where observations from all nodes are available but no links are known), we will also consider link prediction (where some links are observed) and network tomography (where some nodes remain unobserved, relating to latent-variable graphical lasso).