Informative core identification in complex networks

Abstract In a complex network, the core component with interesting structures is usually hidden within noninformative connections. The noises and bias introduced by can obscure salient structure limit many network modeling procedures’ effectiveness. This paper introduces novel core–periphery model for periphery of networks without imposing specific form core. We propose spectral algorithms identification general downstream analysis tasks under model. enjoy strong performance guarantees are scalable large networks. evaluate methods extensive simulation studies demonstrating advantages over multiple traditional methods. also used to extract from citation which results in more interpretable hierarchical community detection.