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- W3207639150 abstract "We consider spectral methods that uncover hidden structures in directed networks. We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model. 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.
- W3207639150 created "2021-10-25" @default.
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- W3207639150 date "2021-10-01" @default.
- W3207639150 modified "2023-10-15" @default.
- W3207639150 title "Directed network Laplacians and random graph models" @default.
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- W3207639150 doi "https://doi.org/10.1098/rsos.211144" @default.
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