Degree-corrected stochastic block models and reliability in networks

Xue Zhang, Xiaojie Wang, Chengli Zhao, Dongyun Yi and Zheng Xie

Physica A: Statistical Mechanics and its Applications, 2014, vol. 393, issue C, 553-559

Abstract: Plenty of algorithms for link prediction have been proposed to extract missing information, identify spurious interactions, reconstruct networks, and so on. Stochastic block models are one of the most accurate methods among all of them. However, this algorithm is designed only for simple graphs and ignores the variation in node degree which is typically displayed in real-world networks. In this paper, we propose a corresponding reliable approach based on degree-corrected stochastic block models, which could be applied in networks containing both multi-edges and self-edges. Empirical comparison on five disparate networks shows that the overall performance of our method is better than the original version in predicting missing links, especially for the interactions between high-degree nodes.

Keywords: Stochastic block models; Complex networks; Link reliability; Bayesian estimation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:393:y:2014:i:c:p:553-559

DOI: 10.1016/j.physa.2013.08.061

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