Title: Entropic Spectral Learning in Large Scale Networks,
Abstract: We present a novel algorithm for learning the spectral density of large scale
networks using stochastic trace estimation and the method of maximum entropy.
The complexity of the algorithm is linear in the number of non-zero elements of
the matrix, offering a computational advantage over other algorithms. We apply
our algorithm to the problem of community detection in large networks. We show
state-of-the-art performance on both synthetic and real datasets.