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

ABSTRACT: 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 networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null mod

SUBMITTER: Humphries MD 

PROVIDER: S-EPMC8253422 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature