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New method optimizes multi-layer network clustering under mixture block models

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Researchers have developed a novel two-stage estimator for clustering multi-layer networks under mixture block models. This method combines tensor-based initialization, node and sample splitting, and a likelihood-based refinement to achieve optimal network clustering error rates, even under extreme sparsity. The framework has been extended to handle count-weighted networks using a mixture multi-layer Poisson block model and has been demonstrated through numerical studies and real-data analyses. AI

RANK_REASON The cluster contains an academic paper detailing a new statistical method for network analysis. [lever_c_demoted from research: ic=1 ai=0.4]

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New method optimizes multi-layer network clustering under mixture block models

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The cluster contains an academic paper detailing a new statistical method for network analysis. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhongyuan Lyu, Ting Li, Dong Xia ·

    Optimal Clustering of Multi-layer Networks under Mixture Block Models

    arXiv:2311.15598v2 Announce Type: replace-cross Abstract: We study clustering for multi-layer networks, where each observation is an undirected network measured on the same set of vertices. Under the mixture multi-layer stochastic block model (MMSBM), each network is generated fr…

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