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Consistent Community Detection in Inter-Layer Dependent Multi-Layer Networks
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PRE
AI
DOI：10.1080/01621459.2024.2308848.png)
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摘要
En 中文
Community detection in multi-layer networks, which aims at finding groups of nodes with similar connective patterns among all layers, has attracted tremendous interests in multi-layer network analysis. Most existing methods are extended from those for single-layer networks, which assume that different layers are independent. In this article, we propose a novel community detection method in multi-layer networks with inter-layer dependence, which integrates the stochastic block model (SBM) and the Ising model. The community structure is modeled by the SBM model and the inter-layer dependence is incorporated via the Ising model. An efficient alternative updating algorithm is developed to tackle the resultant optimization task. Moreover, the asymptotic consistencies of the proposed method in terms of both parameter estimation and community detection are established, which are supported by extensive simulated examples and a real example on a multi-layer malaria parasite gene network. Supplementary materials for this article are available online.
Keyword:
Community detection
Ising model
Multi-layer network
Pseudo-likelihood
Stochastic block model
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
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引用论文
COMMUNITY DETECTION ON MIXTURE MULTILAYER NETWORKS VIA REGULARIZED TENSOR DECOMPOSITION.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
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