Informative core identification in complex networks
This work addresses network analysis challenges for researchers and practitioners by providing a scalable preprocessing method to enhance modeling accuracy, though it is incremental as it builds on existing core-periphery approaches.
The paper tackles the problem of identifying informative core structures in complex networks, where noise from non-informative components can obscure analysis, by introducing a novel core-periphery model and spectral algorithms that achieve strong theoretical guarantees and scalability, as demonstrated through simulations and application to a citation network for improved downstream community detection.
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.