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- W3177924255 abstract "We consider spectral methods that uncover hidden structures in directed networks. We develop a general framework that allows us to associate methods based on optimization formulations with maximum likelihood problems on random graphs. We focus on two existing spectral approaches that build and analyse Laplacian-style matrices via the minimization of frustration and trophic incoherence. These algorithms aim to reveal directed periodic and linear hierarchies, respectively. We show that reordering nodes using the two algorithms, or mapping them onto a specified lattice, is associated with new classes of directed random graph models. Using this random graph setting, we are able to compare the two algorithms on a given network and quantify which structure is more likely to be present. We illustrate the approach on synthetic and real networks, and discuss practical implementation issues." @default.
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- W3177924255 date "2021-07-07" @default.
- W3177924255 modified "2023-09-27" @default.
- W3177924255 title "Directed Network Laplacians and Random Graph Models" @default.
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