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 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Alexandria
Taylor & Francis
02.04.2024
Taylor & Francis Ltd |
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| ISSN: | 1061-8600, 1537-2715 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Chen, Xiao Chen, Yu Zhang, Weiping Hu, Jie |
| Author_xml | – sequence: 1 givenname: Jie surname: Hu fullname: Hu, Jie organization: International Institute of Finance, School of Management, University of Science and Technology of China – sequence: 2 givenname: Xiao surname: Chen fullname: Chen, Xiao organization: International Institute of Finance, School of Management, University of Science and Technology of China – sequence: 3 givenname: Yu surname: Chen fullname: Chen, Yu organization: International Institute of Finance, School of Management, University of Science and Technology of China – sequence: 4 givenname: Weiping surname: Zhang fullname: Zhang, Weiping organization: International Institute of Finance, School of Management, University of Science and Technology of China |
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| SubjectTerms | Algorithms Binary mixtures Community detection EM algorithm Kullback-Leibler divergence Maximum likelihood estimates Mixture distributions Network reconstruction Reconstruction Synergistic effect |
| Title | Joint Network Reconstruction and Community Detection from Rich but Noisy Data |
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