Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models

@article{Humphries2021SpectralEF,
  title={Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models},
  author={M. Humphries and J. A. Caballero and M. Evans and S. Maggi and Abhinav Singh},
  journal={PLoS ONE},
  year={2021},
  volume={16}
}
Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic… Expand

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