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Learning low-rank latent mesoscale structures in networks

Learning low-rank latent mesoscale structures in networks https://ift.tt/VEWrvN7 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.

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