Learning low-rank latent mesoscale structures in networks

We present a new approach to describe low-rank mesoscale structures in networks. We find that many real-world networks possess a small set of `latent motifs' that effectively approximate most subgraphs at a fixed mesoscale. Our work has applications in network comparison and network denoising.