Informative core identification in complex networks

• 成果类型: Article
• 署名作者: Miao, Ruizhong; Li, Tianxi
• 署名单位: University of Virginia
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkac009
• 发表日期: 2023
• 页码: 108-126
• 关键词: periphery structure; community detection; citation networks; coauthorship; Consistency; submatrix; models

摘要

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