Joint Network Reconstruction and Community Detection from Rich but Noisy Data

被引:0
|
作者
Hu, Jie [1 ]
Chen, Xiao [1 ]
Chen, Yu [1 ,2 ]
Zhang, Weiping [1 ]
机构
[1] Univ Sci & Technol China, Int Inst Finance, Sch Management, Hefei, Peoples R China
[2] Univ Sci & Technol China, Int Inst Finance, Sch Management, Hefei 230026, Anhui, Peoples R China
关键词
Community detection; EM algorithm; Kullback-Leibler divergence; Mixture distributions; Network reconstruction; MAXIMUM-LIKELIHOOD-ESTIMATION; MODEL;
D O I
10.1080/10618600.2023.2267630
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:501 / 514
页数:14
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