Joint Network Reconstruction and Community Detection from Rich but Noisy Data

Most empirical studies of complex networks return rich but noisy data, as they measure the network structure repeatedly but with substantial errors due to indirect measurements. In this article, we propose a novel framework, called the group-based binary mixture (GBM) modeling approach, to simultane...

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Vydané v:	Journal of computational and graphical statistics Ročník 33; číslo 2; s. 501 - 514
Hlavní autori:	Hu, Jie, Chen, Xiao, Chen, Yu, Zhang, Weiping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 02.04.2024 Taylor & Francis Ltd
Predmet:	Algorithms Binary mixtures Community detection EM algorithm Kullback-Leibler divergence Maximum likelihood estimates Mixture distributions Network reconstruction Reconstruction Synergistic effect
ISSN:	1061-8600, 1537-2715
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