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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Vydáno v:Journal of computational and graphical statistics Ročník 33; číslo 2; s. 501 - 514
Hlavní autoři: Hu, Jie, Chen, Xiao, Chen, Yu, Zhang, Weiping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Alexandria Taylor & Francis 02.04.2024
Taylor & Francis Ltd
Témata:
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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Shrnutí: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 simultaneously conduct network reconstruction and community detection from such rich but noisy data. A generalized expectation-maximization (EM) algorithm is developed for computing the maximum likelihood estimates, and an information criterion is introduced to consistently select the number of communities. The strong consistency properties of the network reconstruction and community detection are established under some assumption on the Kullback-Leibler (KL) divergence, and in particular, we do not impose assumptions on the true network structure. It is shown that joint reconstruction with community detection has a synergistic effect, whereby actually detecting communities can improve the accuracy of the reconstruction. Finally, we illustrate the performance of the approach with numerical simulations and two real examples. Supplementary materials for this article are available online.
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ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2023.2267630