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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Bibliographic Details

Published in:	Journal of computational and graphical statistics Vol. 33; no. 2; pp. 501 - 514
Main Authors:	Hu, Jie, Chen, Xiao, Chen, Yu, Zhang, Weiping
Format:	Journal Article
Language:	English
Published:	Alexandria Taylor & Francis 02.04.2024 Taylor & Francis Ltd
Subjects:	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
Online Access:	Get full text
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