  In network analysis, the core structure of modeling interest is usually
hidden in a larger network in which most structures are not informative. The
noise and bias introduced by the non-informative component in networks can
obscure the salient structure and limit many network modeling procedures'
effectiveness. This paper introduces a novel core-periphery model for the
non-informative periphery structure of networks without imposing a specific
form for the informative core structure. We propose spectral algorithms for
core identification as a data preprocessing step for general downstream network
analysis tasks based on the model. The algorithm enjoys a strong theoretical
guarantee of accuracy and is scalable for large networks. We evaluate the
proposed method by extensive simulation studies demonstrating various
advantages over many traditional core-periphery methods. The method is applied
to extract the informative core structure from a citation network and give more
informative results in the downstream hierarchical community detection.
