Signal-Noise Separation in Random Matrices by using Eigenvalue
Spectrum Analysis // An algorithm which can be used to
determine an objective threshold for signal-noise separation in
large random matrices (correlation matrices, mutual information
matrices, network adjacency matrices) is provided. The package
makes use of the results of Random Matrix Theory (RMT). The
algorithm increments a suppositional threshold monotonically,
thereby recording the eigenvalue spacing distribution of the
matrix. According to RMT, that distribution undergoes a
characteristic change when the threshold properly separates
signal from noise. By using the algorithm, the modular
structure of a matrix - or of the corresponding network - can
be unraveled.