Informative core identification in complex networks

Ruizhong Miao; Tianxi Li

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arXiv · 2101.06388

Informative core identification in complex networks

Ruizhong Miao·Tianxi Li

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Abstract

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.

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Ruizhong Miao, Tianxi Li. 2021-01-16. Informative core identification in complex networks. https://arxiv.org/abs/2101.06388

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